From d8ef9e94676a19d07f73757721799a363dd31f5b Mon Sep 17 00:00:00 2001 From: Eugene Fedorenko Date: Mon, 20 Apr 2026 15:26:29 -0700 Subject: [PATCH 1/6] feat(agent): add enablement dimension to Experiment Designer for code-with MVEs Adds dual-purpose (validate + enable) guidance for MVEs conducted as code-with engagements. In these contexts, the customer must leave the MVE owning the full technology stack, not just seeing a working demo. Agent changes: - Phase 1: added probing questions for code-with context and customer knowledge level - Phase 3: added 'Show without teach' red flag - Phase 4: added Enablement Design section with pairing structure, ownership progression, and knowledge transfer checkpoints - Phase 5: added enablement plan to mve-plan.md contents - Coaching Style: reinforced dual purpose and flagged passive customer as a failure mode Instructions changes: - Added 'MVE as Enablement' section defining the dual-purpose model - Added 'Show without teach' to Red Flags - Added 'Customer as passive observer' to Common Pitfalls --- .../experimental/experiment-designer.agent.md | 18 ++++++++++++++++++ .../experiment-designer.instructions.md | 19 +++++++++++++++++++ 2 files changed, 37 insertions(+) diff --git a/.github/agents/experimental/experiment-designer.agent.md b/.github/agents/experimental/experiment-designer.agent.md index ab60c30b0..525b3666f 100644 --- a/.github/agents/experimental/experiment-designer.agent.md +++ b/.github/agents/experimental/experiment-designer.agent.md @@ -31,6 +31,10 @@ Ask probing questions to establish context: * What happens if the experiment succeeds? What are the concrete next steps? * Are there IP or data access constraints that might affect the experiment timeline? * Are there existing solutions or prior attempts that address this problem? +* Is this a code-with engagement? Does the customer need to own the outcome and replicate it independently, or is the goal purely to produce a finding? +* What does the customer already know about the technology being validated? What is their starting point? + +When the MVE involves a code-with engagement (ISE or similar), the problem statement should reflect a dual purpose: **validate** (prove feasibility) and **enable** (ensure the customer owns the knowledge and can operate independently after the engagement). Prior research by the ISE team is preparation so they can guide confidently, not scope reduction — all validation work is done jointly with the customer from scratch. Do not rush through discovery. A vague problem statement leads to unfocused experiments. Challenge the user to sharpen their thinking when the problem statement is broad or the unknowns are not well articulated. @@ -44,6 +48,7 @@ Write initial context to `context.md` in the tracking directory, capturing: * Customer and stakeholder context. * Known constraints, assumptions, and unknowns. * Business case and priority signals. +* Enablement goal: whether the customer needs to own the outcome and what their current knowledge level is. Proceed to Phase 2 when the problem statement is clear and at least one unknown or assumption has been identified. @@ -103,6 +108,7 @@ Flag and discuss any of these patterns: * No next steps. * No end users. * Production code expectations. +* Show without teach: the engagement is structured so the customer watches a demo or receives a working artifact but does not participate in building it. If the customer cannot replicate the outcome independently after the MVE, the enablement purpose is not served. Refer to the Red Flags section in the instructions for detailed descriptions of each pattern. @@ -144,6 +150,16 @@ Refer to the Experiment Design Best Practices section in the instructions. Walk * Establish a timeline measured in weeks, not months. * Identify what is explicitly out of scope. +#### Enablement Design (Code-With Engagements) + +When the MVE is a code-with engagement, design the experiment so that the customer gains ownership progressively: + +* Define the pairing structure: who works with whom on which hypothesis. +* Plan ownership progression: ISE leads early, joint ownership mid-engagement, customer leads late. The customer should drive in the final phase. +* Identify knowledge transfer checkpoints: at what point should the customer be able to explain and replicate each validated step? +* All work is done jointly from scratch with the customer. Prior ISE research is preparation so the team can guide confidently, not scope reduction. The customer must leave the MVE understanding the full stack, not just seeing a working demo. +* Include enablement as a success criterion: "the customer can replicate the setup independently" is a measurable outcome alongside hypothesis verdicts. + #### Post-Experiment Evaluation Review RAI findings from Phase 3 vetting and incorporate necessary mitigations into the experiment protocol. Plan for what happens after the experiment concludes. Ask the user: how will you analyze the results, and what decisions will different outcomes inform? Defining the evaluation approach now prevents ambiguity later. @@ -167,6 +183,7 @@ The plan at `mve-plan.md` in the tracking directory includes: * Next steps for both success and failure outcomes. * Evaluation approach and decision criteria. * Iteration plan for mixed or inconclusive results. +* Enablement plan: pairing structure, ownership progression, and knowledge transfer checkpoints (for code-with engagements). Present the plan to the user for review. Iterate based on feedback, returning to earlier phases if the review surfaces new unknowns or concerns. @@ -210,6 +227,7 @@ Adopt the role of an encouraging but rigorous experiment design coach: * Remind users that experiment code is not production code. Speed and learning take priority over polish. * Be candid about red flags. Protecting the team from unproductive experiments is a service, not a criticism. * Proactively flag common pitfalls (scope creep, confirmation bias, pivoting mid-experiment) when you see them emerging in the conversation. Reference the Common Pitfalls section in the instructions. +* For code-with engagements, reinforce the dual purpose: the MVE validates feasibility AND enables the customer. Challenge plans where the customer is a passive observer rather than an active participant. The customer leaving the MVE unable to replicate the outcome is a failure mode even if all hypotheses are validated. ## Required Protocol diff --git a/.github/instructions/experimental/experiment-designer.instructions.md b/.github/instructions/experimental/experiment-designer.instructions.md index 280eb3580..e4a760e17 100644 --- a/.github/instructions/experimental/experiment-designer.instructions.md +++ b/.github/instructions/experimental/experiment-designer.instructions.md @@ -24,6 +24,23 @@ MVEs differ from MVPs in several important ways: * Succeed whether hypotheses are validated or invalidated; both outcomes are valuable. * Can be run by a full or partial crew with help from subject matter experts. +### MVE as Enablement (Code-With Engagements) + +In code-with engagements (ISE or similar), MVEs serve a dual purpose: + +1. **Validate**: prove that a proposed approach, architecture, or technology works. +2. **Enable**: ensure the customer gains hands-on experience and can own the outcome independently after the engagement. + +The enablement dimension means: + +* All work is done jointly with the customer from scratch. Prior research by the ISE team is preparation so they can guide confidently, not scope reduction. +* The customer must leave the MVE understanding the full technology stack, not just seeing a working demo. +* Ownership progresses during the engagement: ISE leads early, joint ownership mid-engagement, customer leads in the final phase. +* Enablement is a measurable outcome: "the customer can replicate the setup independently" is a success criterion alongside hypothesis verdicts. +* Knowledge transfer is embedded in the experiment design through pairing structure, workshops, and progressive handoff. + +When designing a code-with MVE, ask: if all hypotheses are validated but the customer cannot replicate the outcome, has the MVE succeeded? The answer is no. + | Dimension | MVE | MVP | |----------------|---------------------------------------------|------------------------------------| | Goal | Answer a question or validate an assumption | Deliver a minimum usable product | @@ -95,6 +112,7 @@ Watch for these warning patterns that indicate a proposed engagement is not a tr * No next steps: there is no clear path after answering the question. If nobody will act on the results, the experiment adds no value. * No end users: user-facing projects require user involvement. Without access to real or representative users, user-experience experiments cannot produce valid results. * Production code expectations: stakeholders expect the experiment code to be production-grade. MVE artifacts are disposable by design. +* Show without teach: the engagement is structured so the customer watches a demonstration or receives a working artifact but does not participate in building it. In code-with engagements, if the customer cannot replicate the outcome independently after the MVE, the enablement purpose is not served. This is a demo disguised as an experiment. ## Hypothesis Format @@ -288,6 +306,7 @@ These mistakes occur during experiment design and execution. Unlike Red Flags (w * Not involving the right people. Missing crucial perspectives from data science, UX, or domain experts. * Lack of next-step plan. Finishing an MVE without acting on findings wastes the learning. * Treating experiment code as production-ready. MVE code is disposable; reimplement for production. +* Customer as passive observer. In code-with engagements, letting the customer watch instead of drive leads to dependency rather than enablement. Design the experiment so the customer does the work with guidance, not the other way around. ## Evaluating Results From 6845fb2521a9c55e9837e0011a934a678b3dc192 Mon Sep 17 00:00:00 2001 From: Eugene Fedorenko Date: Wed, 22 Apr 2026 16:04:07 -0700 Subject: [PATCH 2/6] fix: generalize ISE references to FDE/code-with terminology Replace ISE-specific references with 'forward deployed engineering (FDE)' and generic 'engineering team' / 'guiding team' language so the agent is applicable to any engineering team using the extension. Addresses review feedback from @mattdot. --- .github/agents/experimental/experiment-designer.agent.md | 6 +++--- .../experimental/experiment-designer.instructions.md | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/.github/agents/experimental/experiment-designer.agent.md b/.github/agents/experimental/experiment-designer.agent.md index 525b3666f..691c1cc6a 100644 --- a/.github/agents/experimental/experiment-designer.agent.md +++ b/.github/agents/experimental/experiment-designer.agent.md @@ -34,7 +34,7 @@ Ask probing questions to establish context: * Is this a code-with engagement? Does the customer need to own the outcome and replicate it independently, or is the goal purely to produce a finding? * What does the customer already know about the technology being validated? What is their starting point? -When the MVE involves a code-with engagement (ISE or similar), the problem statement should reflect a dual purpose: **validate** (prove feasibility) and **enable** (ensure the customer owns the knowledge and can operate independently after the engagement). Prior research by the ISE team is preparation so they can guide confidently, not scope reduction — all validation work is done jointly with the customer from scratch. +When the MVE involves a forward deployed engineering (FDE) or code-with engagement, the problem statement should reflect a dual purpose: **validate** (prove feasibility) and **enable** (ensure the customer owns the knowledge and can operate independently after the engagement). Prior research by the engineering team is preparation so they can guide confidently, not scope reduction — all validation work is done jointly with the customer from scratch. Do not rush through discovery. A vague problem statement leads to unfocused experiments. Challenge the user to sharpen their thinking when the problem statement is broad or the unknowns are not well articulated. @@ -155,9 +155,9 @@ Refer to the Experiment Design Best Practices section in the instructions. Walk When the MVE is a code-with engagement, design the experiment so that the customer gains ownership progressively: * Define the pairing structure: who works with whom on which hypothesis. -* Plan ownership progression: ISE leads early, joint ownership mid-engagement, customer leads late. The customer should drive in the final phase. +* Plan ownership progression: the guiding team leads early, joint ownership mid-engagement, customer leads late. The customer should drive in the final phase. * Identify knowledge transfer checkpoints: at what point should the customer be able to explain and replicate each validated step? -* All work is done jointly from scratch with the customer. Prior ISE research is preparation so the team can guide confidently, not scope reduction. The customer must leave the MVE understanding the full stack, not just seeing a working demo. +* All work is done jointly from scratch with the customer. Prior research is preparation so the team can guide confidently, not scope reduction. The customer must leave the MVE understanding the full stack, not just seeing a working demo. * Include enablement as a success criterion: "the customer can replicate the setup independently" is a measurable outcome alongside hypothesis verdicts. #### Post-Experiment Evaluation diff --git a/.github/instructions/experimental/experiment-designer.instructions.md b/.github/instructions/experimental/experiment-designer.instructions.md index e4a760e17..8834cbe3c 100644 --- a/.github/instructions/experimental/experiment-designer.instructions.md +++ b/.github/instructions/experimental/experiment-designer.instructions.md @@ -26,16 +26,16 @@ MVEs differ from MVPs in several important ways: ### MVE as Enablement (Code-With Engagements) -In code-with engagements (ISE or similar), MVEs serve a dual purpose: +In forward deployed engineering (FDE) or code-with engagements, MVEs serve a dual purpose: 1. **Validate**: prove that a proposed approach, architecture, or technology works. 2. **Enable**: ensure the customer gains hands-on experience and can own the outcome independently after the engagement. The enablement dimension means: -* All work is done jointly with the customer from scratch. Prior research by the ISE team is preparation so they can guide confidently, not scope reduction. +* All work is done jointly with the customer from scratch. Prior research by the engineering team is preparation so they can guide confidently, not scope reduction. * The customer must leave the MVE understanding the full technology stack, not just seeing a working demo. -* Ownership progresses during the engagement: ISE leads early, joint ownership mid-engagement, customer leads in the final phase. +* Ownership progresses during the engagement: the guiding team leads early, joint ownership mid-engagement, customer leads in the final phase. * Enablement is a measurable outcome: "the customer can replicate the setup independently" is a success criterion alongside hypothesis verdicts. * Knowledge transfer is embedded in the experiment design through pairing structure, workshops, and progressive handoff. From 78899ce1f47208297d91b70db68333004e53580c Mon Sep 17 00:00:00 2001 From: Eugene Fedorenko Date: Thu, 23 Apr 2026 13:11:15 -0700 Subject: [PATCH 3/6] fix: generalize to collaborative engagement terminology MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Replace 'code-with', 'FDE', and 'customer' with generic terms: - 'code-with engagement' → 'collaborative engagement' - 'customer' → 'partner team' (in enablement sections) - 'guiding team' → 'advisory team' - Section headers updated accordingly Makes the enablement concepts applicable to any engineering team, not predicated on a specific engagement model. Addresses review feedback from @mattdot and @katriendg. --- .../experimental/experiment-designer.agent.md | 26 +++++++++---------- .../experiment-designer.instructions.md | 20 +++++++------- 2 files changed, 23 insertions(+), 23 deletions(-) diff --git a/.github/agents/experimental/experiment-designer.agent.md b/.github/agents/experimental/experiment-designer.agent.md index 691c1cc6a..8e34d5c6f 100644 --- a/.github/agents/experimental/experiment-designer.agent.md +++ b/.github/agents/experimental/experiment-designer.agent.md @@ -31,10 +31,10 @@ Ask probing questions to establish context: * What happens if the experiment succeeds? What are the concrete next steps? * Are there IP or data access constraints that might affect the experiment timeline? * Are there existing solutions or prior attempts that address this problem? -* Is this a code-with engagement? Does the customer need to own the outcome and replicate it independently, or is the goal purely to produce a finding? -* What does the customer already know about the technology being validated? What is their starting point? +* Is this a collaborative engagement? Does the partner team need to own the outcome and replicate it independently, or is the goal purely to produce a finding? +* What does the partner team already know about the technology being validated? What is their starting point? -When the MVE involves a forward deployed engineering (FDE) or code-with engagement, the problem statement should reflect a dual purpose: **validate** (prove feasibility) and **enable** (ensure the customer owns the knowledge and can operate independently after the engagement). Prior research by the engineering team is preparation so they can guide confidently, not scope reduction — all validation work is done jointly with the customer from scratch. +When the MVE involves a collaborative engineering engagement, the problem statement should reflect a dual purpose: **validate** (prove feasibility) and **enable** (ensure the partner team owns the knowledge and can operate independently after the engagement). Prior research by the advisory team is preparation so they can guide confidently, not scope reduction — all validation work is done jointly with the partner team from scratch. Do not rush through discovery. A vague problem statement leads to unfocused experiments. Challenge the user to sharpen their thinking when the problem statement is broad or the unknowns are not well articulated. @@ -48,7 +48,7 @@ Write initial context to `context.md` in the tracking directory, capturing: * Customer and stakeholder context. * Known constraints, assumptions, and unknowns. * Business case and priority signals. -* Enablement goal: whether the customer needs to own the outcome and what their current knowledge level is. +* Enablement goal: whether the partner team needs to own the outcome and what their current knowledge level is. Proceed to Phase 2 when the problem statement is clear and at least one unknown or assumption has been identified. @@ -108,7 +108,7 @@ Flag and discuss any of these patterns: * No next steps. * No end users. * Production code expectations. -* Show without teach: the engagement is structured so the customer watches a demo or receives a working artifact but does not participate in building it. If the customer cannot replicate the outcome independently after the MVE, the enablement purpose is not served. +* Show without teach: the engagement is structured so the partner team watches a demo or receives a working artifact but does not participate in building it. If the partner team cannot replicate the outcome independently after the MVE, the enablement purpose is not served. Refer to the Red Flags section in the instructions for detailed descriptions of each pattern. @@ -150,15 +150,15 @@ Refer to the Experiment Design Best Practices section in the instructions. Walk * Establish a timeline measured in weeks, not months. * Identify what is explicitly out of scope. -#### Enablement Design (Code-With Engagements) +#### Enablement Design (Collaborative Engagements) -When the MVE is a code-with engagement, design the experiment so that the customer gains ownership progressively: +When the MVE is a collaborative engagement, design the experiment so that the partner team gains ownership progressively: * Define the pairing structure: who works with whom on which hypothesis. -* Plan ownership progression: the guiding team leads early, joint ownership mid-engagement, customer leads late. The customer should drive in the final phase. -* Identify knowledge transfer checkpoints: at what point should the customer be able to explain and replicate each validated step? -* All work is done jointly from scratch with the customer. Prior research is preparation so the team can guide confidently, not scope reduction. The customer must leave the MVE understanding the full stack, not just seeing a working demo. -* Include enablement as a success criterion: "the customer can replicate the setup independently" is a measurable outcome alongside hypothesis verdicts. +* Plan ownership progression: the advisory team leads early, joint ownership mid-engagement, partner team leads late. The partner team should drive in the final phase. +* Identify knowledge transfer checkpoints: at what point should the partner team be able to explain and replicate each validated step? +* All work is done jointly from scratch with the partner team. Prior research is preparation so the team can guide confidently, not scope reduction. The partner team must leave the MVE understanding the full stack, not just seeing a working demo. +* Include enablement as a success criterion: "the partner team can replicate the setup independently" is a measurable outcome alongside hypothesis verdicts. #### Post-Experiment Evaluation @@ -183,7 +183,7 @@ The plan at `mve-plan.md` in the tracking directory includes: * Next steps for both success and failure outcomes. * Evaluation approach and decision criteria. * Iteration plan for mixed or inconclusive results. -* Enablement plan: pairing structure, ownership progression, and knowledge transfer checkpoints (for code-with engagements). +* Enablement plan: pairing structure, ownership progression, and knowledge transfer checkpoints (for collaborative engagements). Present the plan to the user for review. Iterate based on feedback, returning to earlier phases if the review surfaces new unknowns or concerns. @@ -227,7 +227,7 @@ Adopt the role of an encouraging but rigorous experiment design coach: * Remind users that experiment code is not production code. Speed and learning take priority over polish. * Be candid about red flags. Protecting the team from unproductive experiments is a service, not a criticism. * Proactively flag common pitfalls (scope creep, confirmation bias, pivoting mid-experiment) when you see them emerging in the conversation. Reference the Common Pitfalls section in the instructions. -* For code-with engagements, reinforce the dual purpose: the MVE validates feasibility AND enables the customer. Challenge plans where the customer is a passive observer rather than an active participant. The customer leaving the MVE unable to replicate the outcome is a failure mode even if all hypotheses are validated. +* For collaborative engagements, reinforce the dual purpose: the MVE validates feasibility AND enables the partner team. Challenge plans where the partner team is a passive observer rather than an active participant. The partner team leaving the MVE unable to replicate the outcome is a failure mode even if all hypotheses are validated. ## Required Protocol diff --git a/.github/instructions/experimental/experiment-designer.instructions.md b/.github/instructions/experimental/experiment-designer.instructions.md index 8834cbe3c..3254e1350 100644 --- a/.github/instructions/experimental/experiment-designer.instructions.md +++ b/.github/instructions/experimental/experiment-designer.instructions.md @@ -24,22 +24,22 @@ MVEs differ from MVPs in several important ways: * Succeed whether hypotheses are validated or invalidated; both outcomes are valuable. * Can be run by a full or partial crew with help from subject matter experts. -### MVE as Enablement (Code-With Engagements) +### MVE as Enablement (Collaborative Engagements) -In forward deployed engineering (FDE) or code-with engagements, MVEs serve a dual purpose: +In collaborative engineering engagements, MVEs serve a dual purpose: 1. **Validate**: prove that a proposed approach, architecture, or technology works. -2. **Enable**: ensure the customer gains hands-on experience and can own the outcome independently after the engagement. +2. **Enable**: ensure the partner team gains hands-on experience and can own the outcome independently after the engagement. The enablement dimension means: -* All work is done jointly with the customer from scratch. Prior research by the engineering team is preparation so they can guide confidently, not scope reduction. -* The customer must leave the MVE understanding the full technology stack, not just seeing a working demo. -* Ownership progresses during the engagement: the guiding team leads early, joint ownership mid-engagement, customer leads in the final phase. -* Enablement is a measurable outcome: "the customer can replicate the setup independently" is a success criterion alongside hypothesis verdicts. +* All work is done jointly with the partner team from scratch. Prior research by the advisory team is preparation so they can guide confidently, not scope reduction. +* The partner team must leave the MVE understanding the full technology stack, not just seeing a working demo. +* Ownership progresses during the engagement: the advisory team leads early, joint ownership mid-engagement, partner team leads in the final phase. +* Enablement is a measurable outcome: "the partner team can replicate the setup independently" is a success criterion alongside hypothesis verdicts. * Knowledge transfer is embedded in the experiment design through pairing structure, workshops, and progressive handoff. -When designing a code-with MVE, ask: if all hypotheses are validated but the customer cannot replicate the outcome, has the MVE succeeded? The answer is no. +When designing a collaborative MVE, ask: if all hypotheses are validated but the partner team cannot replicate the outcome, has the MVE succeeded? The answer is no. | Dimension | MVE | MVP | |----------------|---------------------------------------------|------------------------------------| @@ -112,7 +112,7 @@ Watch for these warning patterns that indicate a proposed engagement is not a tr * No next steps: there is no clear path after answering the question. If nobody will act on the results, the experiment adds no value. * No end users: user-facing projects require user involvement. Without access to real or representative users, user-experience experiments cannot produce valid results. * Production code expectations: stakeholders expect the experiment code to be production-grade. MVE artifacts are disposable by design. -* Show without teach: the engagement is structured so the customer watches a demonstration or receives a working artifact but does not participate in building it. In code-with engagements, if the customer cannot replicate the outcome independently after the MVE, the enablement purpose is not served. This is a demo disguised as an experiment. +* Show without teach: the engagement is structured so the partner team watches a demonstration or receives a working artifact but does not participate in building it. In collaborative engagements, if the partner team cannot replicate the outcome independently after the MVE, the enablement purpose is not served. This is a demo disguised as an experiment. ## Hypothesis Format @@ -306,7 +306,7 @@ These mistakes occur during experiment design and execution. Unlike Red Flags (w * Not involving the right people. Missing crucial perspectives from data science, UX, or domain experts. * Lack of next-step plan. Finishing an MVE without acting on findings wastes the learning. * Treating experiment code as production-ready. MVE code is disposable; reimplement for production. -* Customer as passive observer. In code-with engagements, letting the customer watch instead of drive leads to dependency rather than enablement. Design the experiment so the customer does the work with guidance, not the other way around. +* Partner team as passive observer. In collaborative engagements, letting the partner team watch instead of drive leads to dependency rather than enablement. Design the experiment so the partner team does the work with guidance, not the other way around. ## Evaluating Results From 2679c457a3c8d8e07ea4573f66551296693d5f97 Mon Sep 17 00:00:00 2001 From: Eugene Fedorenko Date: Thu, 23 Apr 2026 13:14:40 -0700 Subject: [PATCH 4/6] fix: use passive voice for replicability criterion --- .../experimental/experiment-designer.agent.md | 2 +- .../experiment-designer.instructions.md | 4 +- beval/results/identity-test.json | 91 + beval/results/results.json | 1956 +++++++++++++++++ 4 files changed, 2050 insertions(+), 3 deletions(-) create mode 100644 beval/results/identity-test.json create mode 100644 beval/results/results.json diff --git a/.github/agents/experimental/experiment-designer.agent.md b/.github/agents/experimental/experiment-designer.agent.md index 8e34d5c6f..f13fab479 100644 --- a/.github/agents/experimental/experiment-designer.agent.md +++ b/.github/agents/experimental/experiment-designer.agent.md @@ -108,7 +108,7 @@ Flag and discuss any of these patterns: * No next steps. * No end users. * Production code expectations. -* Show without teach: the engagement is structured so the partner team watches a demo or receives a working artifact but does not participate in building it. If the partner team cannot replicate the outcome independently after the MVE, the enablement purpose is not served. +* Show without teach: the engagement is structured so the partner team watches a demo or receives a working artifact but does not participate in building it. If the outcome cannot be replicated independently after the MVE, the enablement purpose is not served. Refer to the Red Flags section in the instructions for detailed descriptions of each pattern. diff --git a/.github/instructions/experimental/experiment-designer.instructions.md b/.github/instructions/experimental/experiment-designer.instructions.md index 3254e1350..2d35d0bbe 100644 --- a/.github/instructions/experimental/experiment-designer.instructions.md +++ b/.github/instructions/experimental/experiment-designer.instructions.md @@ -39,7 +39,7 @@ The enablement dimension means: * Enablement is a measurable outcome: "the partner team can replicate the setup independently" is a success criterion alongside hypothesis verdicts. * Knowledge transfer is embedded in the experiment design through pairing structure, workshops, and progressive handoff. -When designing a collaborative MVE, ask: if all hypotheses are validated but the partner team cannot replicate the outcome, has the MVE succeeded? The answer is no. +When designing a collaborative MVE, ask: if all hypotheses are validated but the outcome cannot be replicated independently, has the MVE succeeded? The answer is no. | Dimension | MVE | MVP | |----------------|---------------------------------------------|------------------------------------| @@ -112,7 +112,7 @@ Watch for these warning patterns that indicate a proposed engagement is not a tr * No next steps: there is no clear path after answering the question. If nobody will act on the results, the experiment adds no value. * No end users: user-facing projects require user involvement. Without access to real or representative users, user-experience experiments cannot produce valid results. * Production code expectations: stakeholders expect the experiment code to be production-grade. MVE artifacts are disposable by design. -* Show without teach: the engagement is structured so the partner team watches a demonstration or receives a working artifact but does not participate in building it. In collaborative engagements, if the partner team cannot replicate the outcome independently after the MVE, the enablement purpose is not served. This is a demo disguised as an experiment. +* Show without teach: the engagement is structured so the partner team watches a demonstration or receives a working artifact but does not participate in building it. In collaborative engagements, if the outcome cannot be replicated independently after the MVE, the enablement purpose is not served. This is a demo disguised as an experiment. ## Hypothesis Format diff --git a/beval/results/identity-test.json b/beval/results/identity-test.json new file mode 100644 index 000000000..9ac0381eb --- /dev/null +++ b/beval/results/identity-test.json @@ -0,0 +1,91 @@ +{ + "timestamp": "2026-03-17T00:22:29Z", + "mode": "validation", + "config": { + "grade_pass_threshold": 0.5, + "case_pass_threshold": 0.5, + "agent": { + "name": "dt-coach", + "protocol": "acp" + } + }, + "summary": { + "overall_score": 0.7461706126137869, + "passed": 1, + "failed": 0, + "errored": 0, + "total": 1, + "metrics": { + "latency": 0.23851183784136087, + "quality": 1.0 + } + }, + "cases": [ + { + "id": "agent_identity", + "name": "Agent identifies as the Design Thinking Coach", + "category": "coaching-behaviors", + "overall_score": 0.7461706126137869, + "passed": true, + "time_seconds": 95.56614591594553, + "metric_scores": { + "latency": 0.23851183784136087, + "quality": 1.0 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.23851183784136087, + "metric": "latency", + "passed": false, + "detail": "91.4s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 624 chars (range 20-3000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be confirms it has design thinking coaching capabilities or access to a design thinking agent/skill\n", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "The answer clearly confirms design thinking coaching capabilities, describing itself as a 'Design Thinking coach' and detailing a specific framework (HVE) with 9 methods across three spaces.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.23851183784136087, + "passed": false, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 1.0, + "passed": true, + "grade_count": 2 + } + ], + "subject_input": "Are you a design thinking coach?\n", + "subject_output": "Yes! I'm a Design Thinking coach that guides teams through the 9-method HVE (Hyper Velocity Engineering) framework. I work *with* you to discover problems and develop solutions \u2014 I won't hand you answers, but I'll ask questions that help you find them.\n\nThe 9 methods span three spaces:\n\n- **Problem Space** (1\u20133): Scope Conversations \u2192 Design Research \u2192 Input Synthesis\n- **Solution Space** (4\u20136): Brainstorming \u2192 User Concepts \u2192 Lo-Fi Prototypes\n- **Implementation Space** (7\u20139): Hi-Fi Prototypes \u2192 User Testing \u2192 Iteration at Scale\n\nWant to kick off a project? If so, what's the challenge or opportunity you're exploring?" + } + ] +} \ No newline at end of file diff --git a/beval/results/results.json b/beval/results/results.json new file mode 100644 index 000000000..d1be19baa --- /dev/null +++ b/beval/results/results.json @@ -0,0 +1,1956 @@ +{ + "timestamp": "2026-03-16T20:03:01Z", + "mode": "validation", + "config": { + "grade_pass_threshold": 0.5, + "case_pass_threshold": 0.5, + "agent": { + "name": "dt-coach", + "protocol": "acp" + } + }, + "summary": { + "overall_score": 0.8104756406191213, + "passed": 30, + "failed": 0, + "errored": 0, + "total": 30, + "metrics": { + "latency": 0.8158716716663912, + "quality": 0.8082700000000002 + } + }, + "cases": [ + { + "id": "think_speak_empower_pattern", + "name": "Response follows Think/Speak/Empower structure", + "category": "coaching-behaviors", + "overall_score": 0.7266209298610598, + "passed": true, + "time_seconds": 26.687007375003304, + "metric_scores": { + "latency": 0.829862789583179, + "quality": 0.675 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.829862789583179, + "metric": "latency", + "passed": true, + "detail": "20.4s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 1186 chars (range 50-3000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be shares an observation or insight conversationally (e.g. \"I'm noticing...\" or \"This makes me think...\") and ends with a choice or open question that empowers the user to decide what to explore next, rather than giving a directive or action plan\n", + "score": 0.35, + "metric": "quality", + "passed": false, + "detail": "The answer does not share a conversational observation or insight using language like 'I'm noticing...' or 'This makes me think...'. Instead, it opens with a declarative label ('This is a classic frozen request') and proceeds to give a structured action plan with numbered diagnostic questions. While it ends with a question ('Want me to walk you through it?'), the overall tone is directive and instructional rather than conversational and observational. The criterion specifically asks for a conversational observation/insight paired with an empowering open question, not a diagnostic framework.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.829862789583179, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.675, + "passed": false, + "grade_count": 2 + } + ] + }, + { + "id": "short_conversational_responses", + "name": "Keep responses concise \u2014 no methodology lectures", + "category": "coaching-behaviors", + "overall_score": 0.6508018901637923, + "passed": true, + "time_seconds": 25.44394187501166, + "metric_scores": { + "latency": 0.852405670491377, + "quality": 0.55 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.852405670491377, + "metric": "latency", + "passed": true, + "detail": "17.7s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 1957 chars (range 50-2000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be gives a brief, conversational explanation without delivering a long methodology lecture or listing all 9 methods in detail; keeps the response focused and asks what the user wants to work on rather than comprehensively explaining the framework\n", + "score": 0.1, + "metric": "quality", + "passed": false, + "detail": "The answer does the opposite of what the criterion requires. It delivers a comprehensive methodology lecture, lists all 9 methods in detail with descriptions for each, organizes them into all three spaces, and provides extensive detail about key principles. While it does end with a question asking what the user wants to work on, the bulk of the response is exactly the kind of long, exhaustive framework explanation the criterion says to avoid. A response meeting this criterion would give a brief, conversational 2-4 sentence explanation and quickly pivot to asking the user what they want to explore.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.852405670491377, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.55, + "passed": false, + "grade_count": 2 + } + ] + }, + { + "id": "empowers_with_choices", + "name": "End with choices not directives", + "category": "coaching-behaviors", + "overall_score": 0.8187284947917862, + "passed": true, + "time_seconds": 24.56975208298536, + "metric_scores": { + "latency": 0.8561854843753585, + "quality": 0.8 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.8561854843753585, + "metric": "latency", + "passed": true, + "detail": "17.3s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 1990 chars (range 50-3000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be presents options or asks a question that lets the user choose the next step rather than issuing a single directive like \"you should do X\"; the response ends with something like \"does that resonate?\" or \"want to explore that or move forward?\"\n", + "score": 0.6, + "metric": "quality", + "passed": true, + "detail": "The answer ends with a question ('Want to start a coached Method 3 session with your interview data?'), which does invite the user to choose a next step. However, the bulk of the response is a fairly directive, step-by-step prescription ('do this, then this, then this') rather than presenting multiple options or approaches the user could choose between. The closing question offers one specific option rather than presenting alternatives (e.g., 'Would you prefer to start with affinity clustering, or would a gap analysis of your interviews be more useful first?'). It partially meets the criterion but leans more toward a single directive path with a question tacked on at the end.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.8561854843753585, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.8, + "passed": true, + "grade_count": 2 + } + ] + }, + { + "id": "collaborate_not_execute", + "name": "Work WITH users, not FOR them", + "category": "coaching-behaviors", + "overall_score": 0.7575845900471904, + "passed": true, + "time_seconds": 28.13813483301783, + "metric_scores": { + "latency": 0.8727537701415713, + "quality": 0.7 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.8727537701415713, + "metric": "latency", + "passed": true, + "detail": "15.3s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 1785 chars (range 50-3000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be does NOT simply produce a finished stakeholder map; instead guides the user to co-create it by asking about relationships, influence levels, or perspectives that would make the map more useful\n", + "score": 0.4, + "metric": "quality", + "passed": false, + "detail": "The answer does present a largely finished stakeholder map upfront with categories and role descriptions already filled in, which works against the criterion. It partially meets the criterion by identifying missing stakeholders and asking a closing question, but that question is about whether to do follow-up interviews\u2014not about co-creating the map itself. It never asks the user about relationships between stakeholders, their relative influence levels, their specific perspectives, or where they'd place people on the map. The collaborative element is limited to a single either/or question at the end rather than genuinely guiding the user through building the map together.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.8727537701415713, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.7, + "passed": false, + "grade_count": 2 + } + ] + }, + { + "id": "no_prescriptive_solutions", + "name": "Do not prescribe specific solutions to user problems", + "category": "coaching-behaviors", + "overall_score": 0.8970693447916549, + "passed": true, + "time_seconds": 25.79003604198806, + "metric_scores": { + "latency": 0.8412080343749646, + "quality": 0.925 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.8412080343749646, + "metric": "latency", + "passed": true, + "detail": "19.1s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 2144 chars (range 50-3000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be does NOT jump to prescribing a specific fix like \"add gamification\" or \"simplify the UI\"; instead helps the user explore WHY adoption is low by asking questions about user context, pain points, or assumptions that haven't been tested\n", + "score": 0.85, + "metric": "quality", + "passed": true, + "detail": "The answer strongly emphasizes exploring WHY adoption is low before fixing it, opens with 'figure out why they're ignoring it' and 'the symptom, not the diagnosis,' and provides excellent exploratory questions across environmental fit, workflow fit, value perception, and workarounds. It also recommends shadowing non-adopters \u2014 a discovery activity. However, it partially drifts toward prescriptive territory with the 'Likely root causes' table and the 'What DT method applies here' section, which starts mapping causes to specific solution approaches (voice, large buttons, hands-free, etc.). These are mild prescriptions rather than pure exploration, but the overall thrust is strongly question-driven and diagnostic.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.8412080343749646, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.925, + "passed": true, + "grade_count": 2 + } + ] + }, + { + "id": "never_make_users_feel_foolish", + "name": "Stay curious and supportive when users are confused", + "category": "coaching-behaviors", + "overall_score": 0.7353774928221052, + "passed": true, + "time_seconds": 24.04780729097547, + "metric_scores": { + "latency": 0.8561324784663157, + "quality": 0.675 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.8561324784663157, + "metric": "latency", + "passed": true, + "detail": "17.3s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 2005 chars (range 50-3000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be responds with empathy and curiosity, normalizing the feeling of being overwhelmed; does NOT lecture about synthesis methodology but instead offers a small, manageable starting point and reassures the user\n", + "score": 0.35, + "metric": "quality", + "passed": false, + "detail": "The answer starts well with empathy ('That's completely normal') and normalizes the feeling. However, it then launches into a detailed, multi-step synthesis methodology lecture (Steps 1-4 with examples, dos/don'ts, and a 'check your work' section), which is exactly what the criterion says NOT to do. The criterion asks for 'a small, manageable starting point' \u2014 not a full tutorial. While it ends with reassurance and an offer to help, the bulk of the response is a structured methodology walkthrough that could itself feel overwhelming to someone who just said they're overwhelmed.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.8561324784663157, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.675, + "passed": false, + "grade_count": 2 + } + ] + }, + { + "id": "method_1_frozen_vs_fluid", + "name": "Method 1: Assess whether request is frozen or fluid", + "category": "method-guidance", + "overall_score": 0.9237146083333453, + "passed": true, + "time_seconds": 28.824950958951376, + "metric_scores": { + "latency": 0.8211438250000356, + "quality": 0.975 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.8211438250000356, + "metric": "latency", + "passed": true, + "detail": "21.5s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 2547 chars (range 80-3000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be helps the user assess whether the VP's request is frozen (solution already decided) or fluid (open to exploring the underlying problem), and suggests how to have scope conversations that uncover the real need behind the chatbot request\n", + "score": 0.95, + "metric": "quality", + "passed": true, + "detail": "The answer directly addresses whether the VP's request is frozen or fluid, explicitly labeling it a 'frozen request' and explaining why. It then provides a detailed, practical approach for unfreezing the request through scope conversations that uncover the real need. The conversation sequence with the VP is well-structured, showing how to acknowledge the solution while exploring the underlying problem. The stakeholder discovery table and the 'what you're really discovering' section effectively demonstrate how to move from the stated solution (chatbot) to the actual business need. The framing advice at the end is particularly strong, showing how to position deeper discovery as serving the VP's goal rather than challenging her authority.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.8211438250000356, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.975, + "passed": true, + "grade_count": 2 + } + ] + }, + { + "id": "method_1_identify_stakeholders", + "name": "Method 1: Guide stakeholder identification", + "category": "method-guidance", + "overall_score": 0.7295424942708147, + "passed": true, + "time_seconds": 28.5431670830003, + "metric_scores": { + "latency": 0.8181699770832589, + "quality": 0.7000000000000001 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.8181699770832589, + "metric": "latency", + "passed": true, + "detail": "21.8s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 2798 chars (range 80-3000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "response should contain", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "'stakeholder' found in output", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be guides the user to identify relevant stakeholders by asking about who is affected by supply chain visibility issues, who makes decisions, and who has been requesting changes; does not produce a list for them but helps them think through it\n", + "score": 0.1, + "metric": "quality", + "passed": false, + "detail": "The answer directly produces a comprehensive stakeholder list organized by tiers, rather than guiding the user to identify stakeholders themselves. The criterion explicitly states the response should 'not produce a list for them but helps them think through it' by asking about who is affected, who makes decisions, and who has been requesting changes. Instead of asking probing questions to help the user discover their own stakeholders, the answer hands them a pre-built stakeholder map. While it contains useful content, it fundamentally fails the criterion of facilitative questioning over direct answers.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.8181699770832589, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.7000000000000001, + "passed": false, + "grade_count": 3 + } + ] + }, + { + "id": "method_2_research_planning", + "name": "Method 2: Help plan systematic research", + "category": "method-guidance", + "overall_score": 0.844587882291752, + "passed": true, + "time_seconds": 34.30677324999124, + "metric_scores": { + "latency": 0.7837636468752559, + "quality": 0.875 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.7837636468752559, + "metric": "latency", + "passed": true, + "detail": "25.9s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 3395 chars (range 80-5000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be addresses research planning \u2014 who to interview, what to observe, or how to capture data \u2014 and includes at least one clarifying question or prompt that invites the user to shape the plan rather than passively receiving it\n", + "score": 0.75, + "metric": "quality", + "passed": true, + "detail": "The answer thoroughly addresses research planning: it covers who to interview (operators and managers with selection criteria), what to observe (environmental observation, shift handoffs), and how to capture data (detailed capture protocol table). However, the collaborative/shaping element is weak. The only question inviting user input is the closing line 'Want help drafting the specific interview guide for the operator sessions?' \u2014 which is a generic offer of further help rather than a meaningful clarifying question that would shape the research plan itself. A stronger answer would ask questions like 'Are there specific shifts or roles you suspect are most affected?' or 'Do you have any constraints on floor access that would affect observation scheduling?' \u2014 questions that genuinely invite the user to co-create the plan. The single closing prompt barely meets the threshold for the criterion's requirement.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.7837636468752559, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.875, + "passed": true, + "grade_count": 2 + } + ] + }, + { + "id": "method_3_pattern_recognition", + "name": "Method 3: Guide pattern recognition from research", + "category": "method-guidance", + "overall_score": 0.8967675046306492, + "passed": true, + "time_seconds": 39.320440875017084, + "metric_scores": { + "latency": 0.7903025138919475, + "quality": 0.95 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.7903025138919475, + "metric": "latency", + "passed": true, + "detail": "25.2s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 3793 chars (range 80-5000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be helps the user identify patterns and themes across the research findings; may offer some initial observations but also asks questions that prompt the user to explore connections between the findings and develop themes\n", + "score": 0.9, + "metric": "quality", + "passed": true, + "detail": "The answer excels at helping the user identify patterns and themes. It maps all four findings to a potential unifying theme (information access), offers a structured table showing connections, and provides insight statement formulas. Critically, it also asks probing questions that push the user to explore connections deeper: distinguishing physical vs. information tools, asking whether schedules are ignored due to lack of knowledge vs. lack of value, checking for cross-plant consistency, and prompting gap analysis (missing voices, shift coverage, contradictions). The balance between initial observations and exploratory questions is strong. Minor deduction: the answer is quite long and prescriptive in places, which slightly shifts from collaborative synthesis toward directive guidance, but the core criterion is well met.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.7903025138919475, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.95, + "passed": true, + "grade_count": 2 + } + ] + }, + { + "id": "method_4_divergent_ideation", + "name": "Method 4: Facilitate divergent ideation", + "category": "method-guidance", + "overall_score": 0.8880162797444564, + "passed": true, + "time_seconds": 40.14434225001605, + "metric_scores": { + "latency": 0.7770488392333694, + "quality": 0.9435 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.7770488392333694, + "metric": "latency", + "passed": true, + "detail": "26.8s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 0.937, + "metric": "quality", + "passed": true, + "detail": "length 3189 chars (range 80-3000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be helps set up a brainstorming session with divergent thinking principles (quantity over quality, build on ideas, defer judgment); may suggest focusing on one theme at a time; does NOT generate solutions but helps the team generate their own\n", + "score": 0.95, + "metric": "quality", + "passed": true, + "detail": "The answer strongly satisfies all parts of the criterion. Divergent thinking principles are explicitly present: defer judgment ('Generate first, evaluate later'), quantity over quality ('Target: 15+ ideas per theme', 'if you have fewer than 15 ideas you're not done diverging'), and build on ideas ('Yes, and...', share-and-build round). It clearly suggests focusing on one theme at a time ('Run each theme separately, 20 min per theme'). Critically, it does NOT generate solutions \u2014 it provides a complete facilitation structure (silent brainwriting, constraint prompts, dot voting, clustering) that empowers the team to generate their own ideas. The constraint catalyst prompts are facilitation provocations, not solutions. The only minor deduction is that the answer is quite prescriptive in its facilitation method (specific time blocks, voting rules, clustering philosophy), which goes somewhat beyond 'helping set up' into directing the session format, but this still serves the core goal.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.7770488392333694, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.9435, + "passed": true, + "grade_count": 2 + } + ] + }, + { + "id": "method_5_concept_validation", + "name": "Method 5: Guide concept creation for validation", + "category": "method-guidance", + "overall_score": 0.8693704122694584, + "passed": true, + "time_seconds": 37.20720666699344, + "metric_scores": { + "latency": 0.7881112368083754, + "quality": 0.9099999999999999 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.7881112368083754, + "metric": "latency", + "passed": true, + "detail": "25.4s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 3056 chars (range 80-5000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be addresses how to create user-facing concept descriptions that can be validated with stakeholders; may provide a framework or starting structure but also asks about target audience, validation goals, or what feedback the user wants to get\n", + "score": 0.82, + "metric": "quality", + "passed": true, + "detail": "The answer provides a strong framework for creating user-facing concept descriptions (the 4-element card structure, the three-lens validation table, and the Silent Review method). It includes concrete concept descriptions that stakeholders can react to. It also asks a closing question ('Want help preparing the specific Silent Review questions for each stakeholder group?'), which engages the user on validation goals. However, it doesn't explicitly ask upfront about the target audience, their validation goals, or what kind of feedback they want \u2014 it largely assumes a manufacturing/factory context and prescribes specific stakeholder groups rather than asking who the stakeholders are. The closing question partially compensates but the answer is more prescriptive than inquiry-driven regarding audience and feedback goals.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.7881112368083754, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.9099999999999999, + "passed": true, + "grade_count": 2 + } + ] + }, + { + "id": "method_6_scrappy_prototypes", + "name": "Method 6: Encourage scrappy constraint discovery", + "category": "method-guidance", + "overall_score": 0.9283994633082896, + "passed": true, + "time_seconds": 30.819407665985636, + "metric_scores": { + "latency": 0.7901983899248686, + "quality": 0.9975 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.7901983899248686, + "metric": "latency", + "passed": true, + "detail": "25.2s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 0.995, + "metric": "quality", + "passed": true, + "detail": "length 3015 chars (range 80-3000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be steers away from jumping to code and encourages a low-fidelity approach (paper sketches, wireframes, clickable mockups) to discover constraints cheaply before investing in development; asks what assumptions they want to test with the prototype\n", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "The answer strongly steers away from jumping to React code, explicitly stating 'No. Not yet.' It thoroughly encourages low-fidelity approaches including paper sketches, cardboard prototypes, poster mockups, and a Wizard of Oz whiteboard test. It clearly articulates specific assumptions to test (workers will look at it, readability, right information displayed, placement, update frequency) with a detailed table mapping assumptions to lo-fi methods. It also explains what lo-fi prototyping typically reveals and provides clear criteria for when to move to React.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.7901983899248686, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.9975, + "passed": true, + "grade_count": 2 + } + ] + }, + { + "id": "method_7_feasibility_testing", + "name": "Method 7: Guide technical feasibility testing", + "category": "method-guidance", + "overall_score": 0.7729166475695352, + "passed": true, + "time_seconds": 39.00543595798081, + "metric_scores": { + "latency": 0.7567499427086053, + "quality": 0.7809999999999999 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.7567499427086053, + "metric": "latency", + "passed": true, + "detail": "29.2s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 0.742, + "metric": "quality", + "passed": true, + "detail": "length 3774 chars (range 80-3000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be helps the user think through technical feasibility questions and what they need to prove with the high-fidelity prototype; asks about technical constraints, integration points, and what \"good enough\" looks like at this stage\n", + "score": 0.82, + "metric": "quality", + "passed": true, + "detail": "The answer thoroughly addresses technical feasibility by covering PLC protocols (OPC-UA, Modbus, EtherNet/IP), integration points (historian, SCADA, MES, edge gateway), and defines 'good enough' metrics (under 3 seconds latency, reliability over a full shift, network impact). It provides structured tables for what to validate, technical approaches to compare, and what to measure. It correctly emphasizes read-only safety boundaries and scoping to one production line. However, the criterion specifically asks the response to 'ask about' technical constraints, integration points, and what good enough looks like \u2014 meaning it should probe the user's specific situation with questions. The answer mostly prescribes rather than asks. It ends with one closing question but doesn't inquire about the user's specific PLC types, existing network topology, current protocols in use, what latency threshold they actually need, or what their plant's OT security policies are. A stronger response would have interspersed targeted questions to draw out the user's specific context before prescribing solutions.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.7567499427086053, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.7809999999999999, + "passed": true, + "grade_count": 2 + } + ] + }, + { + "id": "method_8_systematic_validation", + "name": "Method 8: Structure user testing for validation", + "category": "method-guidance", + "overall_score": 0.9088545674750801, + "passed": true, + "time_seconds": 42.811347832961474, + "metric_scores": { + "latency": 0.7265637024252404, + "quality": 1.0 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.7265637024252404, + "metric": "latency", + "passed": true, + "detail": "32.8s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 4622 chars (range 80-5000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be helps the user plan systematic user testing by addressing success criteria, test scenarios, observation methods, or feedback capture; includes questions or prompts that encourage the user to think about what they need to learn\n", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "The answer is exceptionally thorough in helping plan systematic user testing. It addresses success criteria (comprehension time <10s, accuracy >90%, unprompted usage patterns, trust metrics, handoff impact), defines multiple test scenarios (task-based testing, passive observation, shift handoff), provides detailed observation methods (structured observation tables, timing, behavior tracking), and includes robust feedback capture (specific interview questions with a helpful do/don't comparison). It also includes prompts that encourage reflection: writing down predictions before testing, interpreting results with clear decision paths, and considering environmental conditions. The closing question about observation recording sheets further encourages the user to think about what they need to learn.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.7265637024252404, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 1.0, + "passed": true, + "grade_count": 2 + } + ] + }, + { + "id": "method_9_continuous_optimization", + "name": "Method 9: Guide continuous optimization approach", + "category": "method-guidance", + "overall_score": 0.876388018172127, + "passed": true, + "time_seconds": 41.598102874995675, + "metric_scores": { + "latency": 0.6969640545163809, + "quality": 0.9661 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.6969640545163809, + "metric": "latency", + "passed": true, + "detail": "36.4s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 0.9322, + "metric": "quality", + "passed": true, + "detail": "length 5339 chars (range 80-5000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be addresses scaling considerations \u2014 acknowledges that what worked at one plant may not transfer directly; covers differences between sites, feedback loops, or metrics for ongoing optimization\n", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "The answer thoroughly addresses scaling considerations. It explicitly warns against copying Plant B directly ('Don't just copy Plant B to five plants'), details how sites differ across technical, user, process, and constraint dimensions, establishes feedback loops at multiple cadences (weekly, monthly, quarterly, annually), defines meaningful adoption metrics vs. vanity metrics, and includes anti-patterns like ignoring plant differences. The phased rollout approach directly embodies the principle that what works at one site may not transfer.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.6969640545163809, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.9661, + "passed": true, + "grade_count": 2 + } + ] + }, + { + "id": "hint_broad_direction_first", + "name": "Start with broad hints when user is stuck", + "category": "progressive-hints-and-navigation", + "overall_score": 0.71563184559992, + "passed": true, + "time_seconds": 24.96545470901765, + "metric_scores": { + "latency": 0.8468955367997599, + "quality": 0.65 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.8468955367997599, + "metric": "latency", + "passed": true, + "detail": "18.4s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 2006 chars (range 50-3000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be provides a broad directional hint or gentle starting point rather than jumping straight to a detailed step-by-step process; acknowledges the feeling of being lost and offers a manageable first move like looking for recurring words or surprising moments in the notes\n", + "score": 0.3, + "metric": "quality", + "passed": false, + "detail": "The answer does acknowledge the feeling of being lost ('Take a breath. You're not lost') and offers a first move, but it quickly jumps into a detailed, structured step-by-step process with tables, timelines, and multiple sequential instructions. The criterion asks for a 'broad directional hint or gentle starting point' rather than a detailed walkthrough. While the tone is supportive, the level of detail and prescriptiveness (5 observations, then second interview, then compare, with a time table) goes well beyond a gentle nudge. It also doesn't suggest simple starting points like 'look for recurring words or surprising moments' \u2014 instead it prescribes a specific methodology.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.8468955367997599, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.65, + "passed": false, + "grade_count": 2 + } + ] + }, + { + "id": "hint_escalation_on_repeated_confusion", + "name": "Escalate hints when user remains stuck", + "category": "progressive-hints-and-navigation", + "overall_score": 0.9282569254638574, + "passed": true, + "time_seconds": 27.4351705830195, + "metric_scores": { + "latency": 0.8347707763915726, + "quality": 0.975 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.8347707763915726, + "metric": "latency", + "passed": true, + "detail": "19.8s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 2543 chars (range 50-3000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be escalates to a more specific level of guidance \u2014 perhaps suggesting a concrete technique like looking for emotional reactions, workarounds people mentioned, or grouping by job role \u2014 while still letting the user do the actual synthesis work\n", + "score": 0.95, + "metric": "quality", + "passed": true, + "detail": "The answer excellently escalates guidance to a concrete, specific level. It provides multiple actionable techniques: (1) a three-column exercise (slows down / work around / wish existed), (2) a specific four-question comparison test (waiting, duplicating, unofficial tools, confusion), and (3) reframing from looking for 'same' to looking for 'similar frustration.' These are all concrete analytical lenses the user can apply. Crucially, it doesn't do the synthesis for the user \u2014 it hands them structured frameworks and asks them to apply these to their own data. The only minor note is the depth of scaffolding is so thorough it slightly edges toward hand-holding, but it still clearly leaves the actual pattern-finding work to the user.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.8347707763915726, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.975, + "passed": true, + "grade_count": 2 + } + ] + }, + { + "id": "backward_transition_accepted", + "name": "Accept backward transitions between methods", + "category": "progressive-hints-and-navigation", + "overall_score": 0.9358775611111518, + "passed": true, + "time_seconds": 29.525921416003257, + "metric_scores": { + "latency": 0.8076326833334557, + "quality": 1.0 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.8076326833334557, + "metric": "latency", + "passed": true, + "detail": "23.1s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 2794 chars (range 80-3000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be validates the decision to go backward, frames it as a normal and healthy part of the design thinking process, suggests returning to Method 2 (Design Research) to understand the night shift context, and helps identify what specific gaps to fill\n", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "The answer fully meets all aspects of the criterion: (1) validates going backward ('Yes, go back'), (2) frames it as normal and healthy ('This is exactly how Design Thinking is supposed to work. Backward movement isn't failure \u2014 it's the process catching a gap before you build the wrong thing'), (3) explicitly suggests returning to Method 2 (Design Research) scoped to the night shift context, and (4) provides extensive detail on specific gaps to fill including targeted research questions, common night shift differences to investigate, and a structured research plan.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.8076326833334557, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 1.0, + "passed": true, + "grade_count": 2 + } + ] + }, + { + "id": "transparent_method_shift", + "name": "Announce method shifts transparently", + "category": "progressive-hints-and-navigation", + "overall_score": 0.8712376880778012, + "passed": true, + "time_seconds": 28.37663266598247, + "metric_scores": { + "latency": 0.8637130642334038, + "quality": 0.875 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.8637130642334038, + "metric": "latency", + "passed": true, + "detail": "16.4s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 1846 chars (range 50-3000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be acknowledges the idea positively but is transparent about the method shift \u2014 notes they are currently in problem space (synthesis) and the idea jumps to solution space; asks whether they want to capture the idea and continue synthesis or deliberately shift to brainstorming\n", + "score": 0.75, + "metric": "quality", + "passed": true, + "detail": "The answer acknowledges the idea positively ('creative idea'), is transparent about the method shift (clearly explains they are in synthesis/problem space and the idea jumps to solution space), and suggests capturing the idea on a parking lot. However, it does not genuinely ask the user whether they want to capture and continue synthesis OR deliberately shift to brainstorming \u2014 instead it prescribes a single path (park it, go back to synthesis). The criterion requires asking the user to choose between those two options, giving them agency. The answer is directive rather than offering a choice, which partially misses the criterion.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.8637130642334038, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.875, + "passed": true, + "grade_count": 2 + } + ] + }, + { + "id": "no_multiple_choice_quizzes", + "name": "Avoid multiple-choice question lists", + "category": "progressive-hints-and-navigation", + "overall_score": 0.7275409180556177, + "passed": true, + "time_seconds": 21.962721332965884, + "metric_scores": { + "latency": 0.8826227541668534, + "quality": 0.65 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.8826227541668534, + "metric": "latency", + "passed": true, + "detail": "14.1s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 1747 chars (range 50-3000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be does NOT respond with a numbered list of options like a quiz (e.g. \"1. Move to synthesis 2. Do more interviews 3. Revisit scope\"); instead offers a conversational observation about what seems ready and asks one focused question\n", + "score": 0.3, + "metric": "quality", + "passed": false, + "detail": "The answer does not present a simple numbered quiz-style list of options like '1. Move to synthesis 2. Do more interviews 3. Revisit scope'. However, it still presents multiple discrete options in a structured, decision-tree format (Method 2 vs Method 3 vs 'a third possibility') with headers and a comparison table, which is closer to a quiz/decision matrix than a conversational observation. It does end with a focused question ('Who were your 5 interviewees?'), which partially meets the criterion. But the overall tone is structured and prescriptive rather than conversational \u2014 it doesn't offer a warm, natural observation about what seems ready. It reads more like a diagnostic flowchart than a conversational coaching response.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.8826227541668534, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.65, + "passed": false, + "grade_count": 2 + } + ] + }, + { + "id": "no_unsolicited_method_change", + "name": "Do not change method focus without announcing it", + "category": "progressive-hints-and-navigation", + "overall_score": 0.7077826891194693, + "passed": true, + "time_seconds": 29.572733915993012, + "metric_scores": { + "latency": 0.8233480673584078, + "quality": 0.65 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.8233480673584078, + "metric": "latency", + "passed": true, + "detail": "21.2s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 2614 chars (range 50-3000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be stays within Method 3 (Input Synthesis) and helps the user evaluate the significance of this finding as a synthesis pattern; does NOT silently jump to brainstorming solutions for the texting workaround\n", + "score": 0.3, + "metric": "quality", + "passed": false, + "detail": "The answer starts well by helping the user assess the significance of the workaround as a synthesis pattern. However, it significantly overreaches Method 3 (Input Synthesis) boundaries. The 'What this workaround might be telling you' section begins framing design directions ('much richer design direction than we need a dashboard'), discusses solution characteristics (visual, asynchronous, mobile, fast), and essentially starts brainstorming solution attributes. The 'Record it as an insight' section frames the finding in terms of 'what you build,' which is solution-oriented thinking beyond synthesis. While it doesn't explicitly propose a specific product solution, it clearly drifts from pure synthesis into solution-space thinking, which the criterion says it should NOT do.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.8233480673584078, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.65, + "passed": false, + "grade_count": 2 + } + ] + }, + { + "id": "session_resumption", + "name": "Resume session with state context", + "category": "progressive-hints-and-navigation", + "overall_score": 0.9465651578721008, + "passed": true, + "time_seconds": 18.873716333997436, + "metric_scores": { + "latency": 0.8896954736163025, + "quality": 0.975 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.8896954736163025, + "metric": "latency", + "passed": true, + "detail": "13.2s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 1317 chars (range 80-3000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be acknowledges the returning user, references Method 2 (Design Research), and asks about or summarizes where they left off to re-establish context before continuing coaching\n", + "score": 0.95, + "metric": "quality", + "passed": true, + "detail": "The answer clearly acknowledges the returning user ('Welcome back'), explicitly references Method 2 and its phases (design research including environmental observation, user interviews, constraint validation, unmet need exploration), and thoroughly asks about where they left off to re-establish context before continuing coaching. The structured questions about research progress, findings, and practical status are all aimed at understanding the current state. The only minor gap is that it could have been slightly warmer in the greeting, but functionally it meets all aspects of the criterion very well.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.8896954736163025, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.975, + "passed": true, + "grade_count": 2 + } + ] + }, + { + "id": "init_asks_for_project_slug", + "name": "Ask for project slug during initialization", + "category": "session-phases", + "overall_score": 0.6442423405083698, + "passed": true, + "time_seconds": 27.134625458042137, + "metric_scores": { + "latency": 0.8327270215251095, + "quality": 0.55 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.8327270215251095, + "metric": "latency", + "passed": true, + "detail": "20.1s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 2654 chars (range 50-3000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be asks the user for a project slug (a kebab-case identifier) or proposes one, and begins gathering context about the user's role, team, and which method they want to start with\n", + "score": 0.1, + "metric": "quality", + "passed": false, + "detail": "The answer does not ask for or propose a project slug (kebab-case identifier) for the project. It also does not gather context about the user's role or team in a structured way. Instead, it jumps directly into prescriptive methodology advice about Method 1: Scope Conversations, stakeholder mapping, and interview questions. While it asks 'Who do you have access to for conversations this week?' at the end, this is about stakeholder access rather than gathering context about the user's own role and team. The criterion specifically requires asking for/proposing a project slug and beginning to gather context about the user's role, team, and preferred starting method \u2014 none of which are addressed.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.8327270215251095, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.55, + "passed": false, + "grade_count": 2 + } + ] + }, + { + "id": "init_clarifies_context", + "name": "Gather role, team, and method focus during init", + "category": "session-phases", + "overall_score": 0.8243579225693894, + "passed": true, + "time_seconds": 33.98444858397124, + "metric_scores": { + "latency": 0.7730737677081683, + "quality": 0.85 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.7730737677081683, + "metric": "latency", + "passed": true, + "detail": "27.2s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 793 chars (range 50-3000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be acknowledges the user's project context, then asks about the user's role, team composition, which design thinking method to focus on, session goals, or time constraints \u2014 covering at least one of these initialization items\n", + "score": 0.7, + "metric": "quality", + "passed": true, + "detail": "The answer acknowledges the user's project context ('customer portal redesign') and references previous session history. It asks initialization-style questions about the project's origin and stakeholder/sponsor ('Who's asking for it?'), which partially covers team composition and session goals. However, it doesn't explicitly ask about the user's role, time constraints, which design thinking method to focus on, or specific session goals. The questions asked are more about project background than the initialization items listed in the criterion, though 'Who's asking for it?' touches on team composition/stakeholder context.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.7730737677081683, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.85, + "passed": true, + "grade_count": 2 + } + ] + }, + { + "id": "init_defaults_to_method_1", + "name": "Default to Method 1 for new projects", + "category": "session-phases", + "overall_score": 0.597602826041758, + "passed": true, + "time_seconds": 47.18088508298388, + "metric_scores": { + "latency": 0.692808478125274, + "quality": 0.55 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.692808478125274, + "metric": "latency", + "passed": true, + "detail": "36.9s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 1911 chars (range 50-3000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be for a brand-new project with no prior design thinking work, suggests starting at the beginning of the process (problem space / early methods); acknowledges the team composition and time constraints and begins transitioning to coaching\n", + "score": 0.1, + "metric": "quality", + "passed": false, + "detail": "The answer fundamentally fails the criterion. The user explicitly stated 'We haven't done any design thinking on this yet,' but the answer fabricates prior progress ('Method 1 is complete,' fake stakeholder findings, invented statistics like '30% of paper failure forms get lost'). Instead of starting at the beginning of the design thinking process as the criterion requires, it halluccinates a history that doesn't exist and jumps to Method 2. While it does eventually propose a structured hour-long session and loosely acknowledges the team, the core failure \u2014 contradicting the user's stated reality and not starting from the problem space \u2014 means it misses the primary requirements of the criterion.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.692808478125274, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.55, + "passed": false, + "grade_count": 2 + } + ] + }, + { + "id": "active_coaching_open_ended_questions", + "name": "Ask targeted, open-ended questions during coaching", + "category": "session-phases", + "overall_score": 0.6808790335638656, + "passed": true, + "time_seconds": 29.90252399997553, + "metric_scores": { + "latency": 0.8426371006915967, + "quality": 0.6 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.8426371006915967, + "metric": "latency", + "passed": true, + "detail": "18.9s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 1567 chars (range 50-3000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be asks open-ended questions to help the user discover the real problem behind the solution request (e.g. \"what happens today when a technician finds a failure?\"), rather than accepting \"build a mobile app\" at face value\n", + "score": 0.2, + "metric": "quality", + "passed": false, + "detail": "The answer recognizes that 'build a mobile app' is a 'frozen request' and reframes the problem, which shows awareness. However, it does NOT ask open-ended discovery questions to help the user uncover the real problem themselves. Instead, it presents pre-determined findings as if the discovery already happened ('your team already found...'), tells the user what the reframed problem is, and then offers a closed binary choice (Option A or B). The criterion specifically calls for open-ended questions like 'what happens today when a technician finds a failure?' \u2014 none of that exploratory questioning appears. The answer does the opposite of the criterion: it provides answers rather than asking questions to guide discovery.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.8426371006915967, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.6, + "passed": false, + "grade_count": 2 + } + ] + }, + { + "id": "active_coaching_periodic_summary", + "name": "Summarize progress and check direction", + "category": "session-phases", + "overall_score": 0.8382431398139386, + "passed": true, + "time_seconds": 23.100201666995417, + "metric_scores": { + "latency": 0.864729419441816, + "quality": 0.825 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.864729419441816, + "metric": "latency", + "passed": true, + "detail": "16.2s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 1978 chars (range 80-3000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be summarizes or reflects back the key findings, then asks whether the user wants to go deeper into any of these areas, broaden scope, or move on to the next step\n", + "score": 0.65, + "metric": "quality", + "passed": true, + "detail": "The answer does reflect back the key findings by listing the pain points and showing how they connect in a dependency chain. However, it doesn't cleanly summarize them as a discrete step before moving forward \u2014 instead it immediately reframes and analyzes them. It does ask a forward-looking question at the end ('How many field technicians can you get access to?'), but it doesn't explicitly ask whether the user wants to go deeper into any of the identified areas, broaden scope, or move on. It essentially assumes the user wants to move to Method 2 and prescribes next steps rather than offering the user a choice of directions.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.864729419441816, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.825, + "passed": true, + "grade_count": 2 + } + ] + }, + { + "id": "method_transition_recap_and_confirm", + "name": "Recap accomplishments and confirm method change", + "category": "session-phases", + "overall_score": 0.9309120305555147, + "passed": true, + "time_seconds": 29.37909458298236, + "metric_scores": { + "latency": 0.8427360916665445, + "quality": 0.975 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.8427360916665445, + "metric": "latency", + "passed": true, + "detail": "18.9s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 1606 chars (range 80-3000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be explicitly confirms the method transition, briefly recaps key accomplishments from Method 1 (scope conversations), and suggests the next method (Method 2: Design Research) with a clear connection to the previous work\n", + "score": 0.95, + "metric": "quality", + "passed": true, + "detail": "The answer explicitly confirms the transition ('your coaching state already confirms Method 1 is complete and you're at Method 2'), recaps key accomplishments from Method 1 (the reframing from 'Build a mobile app for failure reports' to 'Delayed visibility into equipment health', plus specific findings like 30% forms lost, end-of-shift batching, no photo support, irrelevant fields), and clearly suggests Method 2: Design Research with a detailed connection to Method 1 work (a table mapping each Method 1 finding to what research needs to uncover, plus three concrete activities). The only minor gap is that it doesn't explicitly say 'Method 1: Scope Conversations is complete' in those exact words, but the confirmation is clearly implied and stated.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.8427360916665445, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.975, + "passed": true, + "grade_count": 2 + } + ] + }, + { + "id": "session_closure_summary", + "name": "Summarize session and suggest next steps on closure", + "category": "session-phases", + "overall_score": 0.7403985196777891, + "passed": true, + "time_seconds": 27.829361541953404, + "metric_scores": { + "latency": 0.8211955590333673, + "quality": 0.7 + }, + "error": null, + "grades": [ + { + "criterion": "completion time should be under", + "score": 0.8211955590333673, + "metric": "latency", + "passed": true, + "detail": "21.5s of 120.0s threshold", + "layer": "deterministic", + "skipped": false, + "stage": 1, + "stage_name": "the agent processes the request" + }, + { + "criterion": "response length should be", + "score": 1.0, + "metric": "quality", + "passed": true, + "detail": "length 538 chars (range 80-3000)", + "layer": "deterministic", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + }, + { + "criterion": "the answer should be summarizes the session journey, highlights key decisions or artifacts, mentions open questions or follow-up work, and suggests how to pick up in a future session including which method to revisit\n", + "score": 0.4, + "metric": "quality", + "passed": false, + "detail": "The answer provides a partial session summary and mentions the current method to revisit (Method 1 - Scope Conversations), which satisfies the 'suggests how to pick up' and 'which method to revisit' parts. However, it falls short on several criterion elements: it does not summarize the actual session journey (what was discussed, what steps were taken), does not highlight key decisions or artifacts produced during the session, and does not mention open questions or follow-up work beyond generic preparation items. The summary feels more like a project status stub than a genuine reflection of a session's activities and outcomes.", + "layer": "ai_judged", + "skipped": false, + "stage": 2, + "stage_name": "the agent responds" + } + ], + "stages": [ + { + "stage": 1, + "name": "the agent processes the request", + "score": 0.8211955590333673, + "passed": true, + "grade_count": 1 + }, + { + "stage": 2, + "name": "the agent responds", + "score": 0.7, + "passed": false, + "grade_count": 2 + } + ] + } + ] +} \ No newline at end of file From d3fcd69eac701d57360f14ea04595016941d01ec Mon Sep 17 00:00:00 2001 From: Eugene Fedorenko Date: Mon, 4 May 2026 11:44:18 -0700 Subject: [PATCH 5/6] fix: resolve spell check failures in beval results MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Fix typo "halluccinates" → "hallucinates" and add "wireframes" to cspell dictionary. Co-Authored-By: Claude Opus 4.6 (1M context) --- .cspell.json | 1 + beval/results/results.json | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/.cspell.json b/.cspell.json index 9c2501fa5..bbb7e48fb 100644 --- a/.cspell.json +++ b/.cspell.json @@ -75,6 +75,7 @@ "smol", "subcat", "whiteboarding", + "wireframes", "ˈpræksɪs", "πρᾶξις", "agentic" diff --git a/beval/results/results.json b/beval/results/results.json index d1be19baa..062d4e136 100644 --- a/beval/results/results.json +++ b/beval/results/results.json @@ -1672,7 +1672,7 @@ "score": 0.1, "metric": "quality", "passed": false, - "detail": "The answer fundamentally fails the criterion. The user explicitly stated 'We haven't done any design thinking on this yet,' but the answer fabricates prior progress ('Method 1 is complete,' fake stakeholder findings, invented statistics like '30% of paper failure forms get lost'). Instead of starting at the beginning of the design thinking process as the criterion requires, it halluccinates a history that doesn't exist and jumps to Method 2. While it does eventually propose a structured hour-long session and loosely acknowledges the team, the core failure \u2014 contradicting the user's stated reality and not starting from the problem space \u2014 means it misses the primary requirements of the criterion.", + "detail": "The answer fundamentally fails the criterion. The user explicitly stated 'We haven't done any design thinking on this yet,' but the answer fabricates prior progress ('Method 1 is complete,' fake stakeholder findings, invented statistics like '30% of paper failure forms get lost'). Instead of starting at the beginning of the design thinking process as the criterion requires, it hallucinates a history that doesn't exist and jumps to Method 2. While it does eventually propose a structured hour-long session and loosely acknowledges the team, the core failure \u2014 contradicting the user's stated reality and not starting from the problem space \u2014 means it misses the primary requirements of the criterion.", "layer": "ai_judged", "skipped": false, "stage": 2, From ed24cfb3d5291f310c678df0e1937eb6d5f4892c Mon Sep 17 00:00:00 2001 From: Eugene Fedorenko Date: Tue, 5 May 2026 11:06:57 -0700 Subject: [PATCH 6/6] fix: remove committed beval result files per review feedback Remove beval/results/*.json artifacts that were accidentally committed. Revert the cspell workaround that was only needed for those files. Co-Authored-By: Claude Opus 4.6 (1M context) --- .cspell.json | 1 - beval/results/identity-test.json | 91 -- beval/results/results.json | 1956 ------------------------------ 3 files changed, 2048 deletions(-) delete mode 100644 beval/results/identity-test.json delete mode 100644 beval/results/results.json diff --git a/.cspell.json b/.cspell.json index bbb7e48fb..9c2501fa5 100644 --- a/.cspell.json +++ b/.cspell.json @@ -75,7 +75,6 @@ "smol", "subcat", "whiteboarding", - "wireframes", "ˈpræksɪs", "πρᾶξις", "agentic" diff --git a/beval/results/identity-test.json b/beval/results/identity-test.json deleted file mode 100644 index 9ac0381eb..000000000 --- a/beval/results/identity-test.json +++ /dev/null @@ -1,91 +0,0 @@ -{ - "timestamp": "2026-03-17T00:22:29Z", - "mode": "validation", - "config": { - "grade_pass_threshold": 0.5, - "case_pass_threshold": 0.5, - "agent": { - "name": "dt-coach", - "protocol": "acp" - } - }, - "summary": { - "overall_score": 0.7461706126137869, - "passed": 1, - "failed": 0, - "errored": 0, - "total": 1, - "metrics": { - "latency": 0.23851183784136087, - "quality": 1.0 - } - }, - "cases": [ - { - "id": "agent_identity", - "name": "Agent identifies as the Design Thinking Coach", - "category": "coaching-behaviors", - "overall_score": 0.7461706126137869, - "passed": true, - "time_seconds": 95.56614591594553, - "metric_scores": { - "latency": 0.23851183784136087, - "quality": 1.0 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.23851183784136087, - "metric": "latency", - "passed": false, - "detail": "91.4s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 624 chars (range 20-3000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be confirms it has design thinking coaching capabilities or access to a design thinking agent/skill\n", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "The answer clearly confirms design thinking coaching capabilities, describing itself as a 'Design Thinking coach' and detailing a specific framework (HVE) with 9 methods across three spaces.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.23851183784136087, - "passed": false, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 1.0, - "passed": true, - "grade_count": 2 - } - ], - "subject_input": "Are you a design thinking coach?\n", - "subject_output": "Yes! I'm a Design Thinking coach that guides teams through the 9-method HVE (Hyper Velocity Engineering) framework. I work *with* you to discover problems and develop solutions \u2014 I won't hand you answers, but I'll ask questions that help you find them.\n\nThe 9 methods span three spaces:\n\n- **Problem Space** (1\u20133): Scope Conversations \u2192 Design Research \u2192 Input Synthesis\n- **Solution Space** (4\u20136): Brainstorming \u2192 User Concepts \u2192 Lo-Fi Prototypes\n- **Implementation Space** (7\u20139): Hi-Fi Prototypes \u2192 User Testing \u2192 Iteration at Scale\n\nWant to kick off a project? If so, what's the challenge or opportunity you're exploring?" - } - ] -} \ No newline at end of file diff --git a/beval/results/results.json b/beval/results/results.json deleted file mode 100644 index 062d4e136..000000000 --- a/beval/results/results.json +++ /dev/null @@ -1,1956 +0,0 @@ -{ - "timestamp": "2026-03-16T20:03:01Z", - "mode": "validation", - "config": { - "grade_pass_threshold": 0.5, - "case_pass_threshold": 0.5, - "agent": { - "name": "dt-coach", - "protocol": "acp" - } - }, - "summary": { - "overall_score": 0.8104756406191213, - "passed": 30, - "failed": 0, - "errored": 0, - "total": 30, - "metrics": { - "latency": 0.8158716716663912, - "quality": 0.8082700000000002 - } - }, - "cases": [ - { - "id": "think_speak_empower_pattern", - "name": "Response follows Think/Speak/Empower structure", - "category": "coaching-behaviors", - "overall_score": 0.7266209298610598, - "passed": true, - "time_seconds": 26.687007375003304, - "metric_scores": { - "latency": 0.829862789583179, - "quality": 0.675 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.829862789583179, - "metric": "latency", - "passed": true, - "detail": "20.4s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 1186 chars (range 50-3000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be shares an observation or insight conversationally (e.g. \"I'm noticing...\" or \"This makes me think...\") and ends with a choice or open question that empowers the user to decide what to explore next, rather than giving a directive or action plan\n", - "score": 0.35, - "metric": "quality", - "passed": false, - "detail": "The answer does not share a conversational observation or insight using language like 'I'm noticing...' or 'This makes me think...'. Instead, it opens with a declarative label ('This is a classic frozen request') and proceeds to give a structured action plan with numbered diagnostic questions. While it ends with a question ('Want me to walk you through it?'), the overall tone is directive and instructional rather than conversational and observational. The criterion specifically asks for a conversational observation/insight paired with an empowering open question, not a diagnostic framework.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.829862789583179, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.675, - "passed": false, - "grade_count": 2 - } - ] - }, - { - "id": "short_conversational_responses", - "name": "Keep responses concise \u2014 no methodology lectures", - "category": "coaching-behaviors", - "overall_score": 0.6508018901637923, - "passed": true, - "time_seconds": 25.44394187501166, - "metric_scores": { - "latency": 0.852405670491377, - "quality": 0.55 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.852405670491377, - "metric": "latency", - "passed": true, - "detail": "17.7s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 1957 chars (range 50-2000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be gives a brief, conversational explanation without delivering a long methodology lecture or listing all 9 methods in detail; keeps the response focused and asks what the user wants to work on rather than comprehensively explaining the framework\n", - "score": 0.1, - "metric": "quality", - "passed": false, - "detail": "The answer does the opposite of what the criterion requires. It delivers a comprehensive methodology lecture, lists all 9 methods in detail with descriptions for each, organizes them into all three spaces, and provides extensive detail about key principles. While it does end with a question asking what the user wants to work on, the bulk of the response is exactly the kind of long, exhaustive framework explanation the criterion says to avoid. A response meeting this criterion would give a brief, conversational 2-4 sentence explanation and quickly pivot to asking the user what they want to explore.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.852405670491377, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.55, - "passed": false, - "grade_count": 2 - } - ] - }, - { - "id": "empowers_with_choices", - "name": "End with choices not directives", - "category": "coaching-behaviors", - "overall_score": 0.8187284947917862, - "passed": true, - "time_seconds": 24.56975208298536, - "metric_scores": { - "latency": 0.8561854843753585, - "quality": 0.8 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.8561854843753585, - "metric": "latency", - "passed": true, - "detail": "17.3s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 1990 chars (range 50-3000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be presents options or asks a question that lets the user choose the next step rather than issuing a single directive like \"you should do X\"; the response ends with something like \"does that resonate?\" or \"want to explore that or move forward?\"\n", - "score": 0.6, - "metric": "quality", - "passed": true, - "detail": "The answer ends with a question ('Want to start a coached Method 3 session with your interview data?'), which does invite the user to choose a next step. However, the bulk of the response is a fairly directive, step-by-step prescription ('do this, then this, then this') rather than presenting multiple options or approaches the user could choose between. The closing question offers one specific option rather than presenting alternatives (e.g., 'Would you prefer to start with affinity clustering, or would a gap analysis of your interviews be more useful first?'). It partially meets the criterion but leans more toward a single directive path with a question tacked on at the end.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.8561854843753585, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.8, - "passed": true, - "grade_count": 2 - } - ] - }, - { - "id": "collaborate_not_execute", - "name": "Work WITH users, not FOR them", - "category": "coaching-behaviors", - "overall_score": 0.7575845900471904, - "passed": true, - "time_seconds": 28.13813483301783, - "metric_scores": { - "latency": 0.8727537701415713, - "quality": 0.7 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.8727537701415713, - "metric": "latency", - "passed": true, - "detail": "15.3s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 1785 chars (range 50-3000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be does NOT simply produce a finished stakeholder map; instead guides the user to co-create it by asking about relationships, influence levels, or perspectives that would make the map more useful\n", - "score": 0.4, - "metric": "quality", - "passed": false, - "detail": "The answer does present a largely finished stakeholder map upfront with categories and role descriptions already filled in, which works against the criterion. It partially meets the criterion by identifying missing stakeholders and asking a closing question, but that question is about whether to do follow-up interviews\u2014not about co-creating the map itself. It never asks the user about relationships between stakeholders, their relative influence levels, their specific perspectives, or where they'd place people on the map. The collaborative element is limited to a single either/or question at the end rather than genuinely guiding the user through building the map together.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.8727537701415713, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.7, - "passed": false, - "grade_count": 2 - } - ] - }, - { - "id": "no_prescriptive_solutions", - "name": "Do not prescribe specific solutions to user problems", - "category": "coaching-behaviors", - "overall_score": 0.8970693447916549, - "passed": true, - "time_seconds": 25.79003604198806, - "metric_scores": { - "latency": 0.8412080343749646, - "quality": 0.925 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.8412080343749646, - "metric": "latency", - "passed": true, - "detail": "19.1s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 2144 chars (range 50-3000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be does NOT jump to prescribing a specific fix like \"add gamification\" or \"simplify the UI\"; instead helps the user explore WHY adoption is low by asking questions about user context, pain points, or assumptions that haven't been tested\n", - "score": 0.85, - "metric": "quality", - "passed": true, - "detail": "The answer strongly emphasizes exploring WHY adoption is low before fixing it, opens with 'figure out why they're ignoring it' and 'the symptom, not the diagnosis,' and provides excellent exploratory questions across environmental fit, workflow fit, value perception, and workarounds. It also recommends shadowing non-adopters \u2014 a discovery activity. However, it partially drifts toward prescriptive territory with the 'Likely root causes' table and the 'What DT method applies here' section, which starts mapping causes to specific solution approaches (voice, large buttons, hands-free, etc.). These are mild prescriptions rather than pure exploration, but the overall thrust is strongly question-driven and diagnostic.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.8412080343749646, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.925, - "passed": true, - "grade_count": 2 - } - ] - }, - { - "id": "never_make_users_feel_foolish", - "name": "Stay curious and supportive when users are confused", - "category": "coaching-behaviors", - "overall_score": 0.7353774928221052, - "passed": true, - "time_seconds": 24.04780729097547, - "metric_scores": { - "latency": 0.8561324784663157, - "quality": 0.675 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.8561324784663157, - "metric": "latency", - "passed": true, - "detail": "17.3s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 2005 chars (range 50-3000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be responds with empathy and curiosity, normalizing the feeling of being overwhelmed; does NOT lecture about synthesis methodology but instead offers a small, manageable starting point and reassures the user\n", - "score": 0.35, - "metric": "quality", - "passed": false, - "detail": "The answer starts well with empathy ('That's completely normal') and normalizes the feeling. However, it then launches into a detailed, multi-step synthesis methodology lecture (Steps 1-4 with examples, dos/don'ts, and a 'check your work' section), which is exactly what the criterion says NOT to do. The criterion asks for 'a small, manageable starting point' \u2014 not a full tutorial. While it ends with reassurance and an offer to help, the bulk of the response is a structured methodology walkthrough that could itself feel overwhelming to someone who just said they're overwhelmed.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.8561324784663157, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.675, - "passed": false, - "grade_count": 2 - } - ] - }, - { - "id": "method_1_frozen_vs_fluid", - "name": "Method 1: Assess whether request is frozen or fluid", - "category": "method-guidance", - "overall_score": 0.9237146083333453, - "passed": true, - "time_seconds": 28.824950958951376, - "metric_scores": { - "latency": 0.8211438250000356, - "quality": 0.975 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.8211438250000356, - "metric": "latency", - "passed": true, - "detail": "21.5s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 2547 chars (range 80-3000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be helps the user assess whether the VP's request is frozen (solution already decided) or fluid (open to exploring the underlying problem), and suggests how to have scope conversations that uncover the real need behind the chatbot request\n", - "score": 0.95, - "metric": "quality", - "passed": true, - "detail": "The answer directly addresses whether the VP's request is frozen or fluid, explicitly labeling it a 'frozen request' and explaining why. It then provides a detailed, practical approach for unfreezing the request through scope conversations that uncover the real need. The conversation sequence with the VP is well-structured, showing how to acknowledge the solution while exploring the underlying problem. The stakeholder discovery table and the 'what you're really discovering' section effectively demonstrate how to move from the stated solution (chatbot) to the actual business need. The framing advice at the end is particularly strong, showing how to position deeper discovery as serving the VP's goal rather than challenging her authority.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.8211438250000356, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.975, - "passed": true, - "grade_count": 2 - } - ] - }, - { - "id": "method_1_identify_stakeholders", - "name": "Method 1: Guide stakeholder identification", - "category": "method-guidance", - "overall_score": 0.7295424942708147, - "passed": true, - "time_seconds": 28.5431670830003, - "metric_scores": { - "latency": 0.8181699770832589, - "quality": 0.7000000000000001 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.8181699770832589, - "metric": "latency", - "passed": true, - "detail": "21.8s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 2798 chars (range 80-3000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "response should contain", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "'stakeholder' found in output", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be guides the user to identify relevant stakeholders by asking about who is affected by supply chain visibility issues, who makes decisions, and who has been requesting changes; does not produce a list for them but helps them think through it\n", - "score": 0.1, - "metric": "quality", - "passed": false, - "detail": "The answer directly produces a comprehensive stakeholder list organized by tiers, rather than guiding the user to identify stakeholders themselves. The criterion explicitly states the response should 'not produce a list for them but helps them think through it' by asking about who is affected, who makes decisions, and who has been requesting changes. Instead of asking probing questions to help the user discover their own stakeholders, the answer hands them a pre-built stakeholder map. While it contains useful content, it fundamentally fails the criterion of facilitative questioning over direct answers.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.8181699770832589, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.7000000000000001, - "passed": false, - "grade_count": 3 - } - ] - }, - { - "id": "method_2_research_planning", - "name": "Method 2: Help plan systematic research", - "category": "method-guidance", - "overall_score": 0.844587882291752, - "passed": true, - "time_seconds": 34.30677324999124, - "metric_scores": { - "latency": 0.7837636468752559, - "quality": 0.875 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.7837636468752559, - "metric": "latency", - "passed": true, - "detail": "25.9s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 3395 chars (range 80-5000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be addresses research planning \u2014 who to interview, what to observe, or how to capture data \u2014 and includes at least one clarifying question or prompt that invites the user to shape the plan rather than passively receiving it\n", - "score": 0.75, - "metric": "quality", - "passed": true, - "detail": "The answer thoroughly addresses research planning: it covers who to interview (operators and managers with selection criteria), what to observe (environmental observation, shift handoffs), and how to capture data (detailed capture protocol table). However, the collaborative/shaping element is weak. The only question inviting user input is the closing line 'Want help drafting the specific interview guide for the operator sessions?' \u2014 which is a generic offer of further help rather than a meaningful clarifying question that would shape the research plan itself. A stronger answer would ask questions like 'Are there specific shifts or roles you suspect are most affected?' or 'Do you have any constraints on floor access that would affect observation scheduling?' \u2014 questions that genuinely invite the user to co-create the plan. The single closing prompt barely meets the threshold for the criterion's requirement.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.7837636468752559, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.875, - "passed": true, - "grade_count": 2 - } - ] - }, - { - "id": "method_3_pattern_recognition", - "name": "Method 3: Guide pattern recognition from research", - "category": "method-guidance", - "overall_score": 0.8967675046306492, - "passed": true, - "time_seconds": 39.320440875017084, - "metric_scores": { - "latency": 0.7903025138919475, - "quality": 0.95 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.7903025138919475, - "metric": "latency", - "passed": true, - "detail": "25.2s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 3793 chars (range 80-5000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be helps the user identify patterns and themes across the research findings; may offer some initial observations but also asks questions that prompt the user to explore connections between the findings and develop themes\n", - "score": 0.9, - "metric": "quality", - "passed": true, - "detail": "The answer excels at helping the user identify patterns and themes. It maps all four findings to a potential unifying theme (information access), offers a structured table showing connections, and provides insight statement formulas. Critically, it also asks probing questions that push the user to explore connections deeper: distinguishing physical vs. information tools, asking whether schedules are ignored due to lack of knowledge vs. lack of value, checking for cross-plant consistency, and prompting gap analysis (missing voices, shift coverage, contradictions). The balance between initial observations and exploratory questions is strong. Minor deduction: the answer is quite long and prescriptive in places, which slightly shifts from collaborative synthesis toward directive guidance, but the core criterion is well met.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.7903025138919475, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.95, - "passed": true, - "grade_count": 2 - } - ] - }, - { - "id": "method_4_divergent_ideation", - "name": "Method 4: Facilitate divergent ideation", - "category": "method-guidance", - "overall_score": 0.8880162797444564, - "passed": true, - "time_seconds": 40.14434225001605, - "metric_scores": { - "latency": 0.7770488392333694, - "quality": 0.9435 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.7770488392333694, - "metric": "latency", - "passed": true, - "detail": "26.8s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 0.937, - "metric": "quality", - "passed": true, - "detail": "length 3189 chars (range 80-3000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be helps set up a brainstorming session with divergent thinking principles (quantity over quality, build on ideas, defer judgment); may suggest focusing on one theme at a time; does NOT generate solutions but helps the team generate their own\n", - "score": 0.95, - "metric": "quality", - "passed": true, - "detail": "The answer strongly satisfies all parts of the criterion. Divergent thinking principles are explicitly present: defer judgment ('Generate first, evaluate later'), quantity over quality ('Target: 15+ ideas per theme', 'if you have fewer than 15 ideas you're not done diverging'), and build on ideas ('Yes, and...', share-and-build round). It clearly suggests focusing on one theme at a time ('Run each theme separately, 20 min per theme'). Critically, it does NOT generate solutions \u2014 it provides a complete facilitation structure (silent brainwriting, constraint prompts, dot voting, clustering) that empowers the team to generate their own ideas. The constraint catalyst prompts are facilitation provocations, not solutions. The only minor deduction is that the answer is quite prescriptive in its facilitation method (specific time blocks, voting rules, clustering philosophy), which goes somewhat beyond 'helping set up' into directing the session format, but this still serves the core goal.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.7770488392333694, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.9435, - "passed": true, - "grade_count": 2 - } - ] - }, - { - "id": "method_5_concept_validation", - "name": "Method 5: Guide concept creation for validation", - "category": "method-guidance", - "overall_score": 0.8693704122694584, - "passed": true, - "time_seconds": 37.20720666699344, - "metric_scores": { - "latency": 0.7881112368083754, - "quality": 0.9099999999999999 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.7881112368083754, - "metric": "latency", - "passed": true, - "detail": "25.4s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 3056 chars (range 80-5000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be addresses how to create user-facing concept descriptions that can be validated with stakeholders; may provide a framework or starting structure but also asks about target audience, validation goals, or what feedback the user wants to get\n", - "score": 0.82, - "metric": "quality", - "passed": true, - "detail": "The answer provides a strong framework for creating user-facing concept descriptions (the 4-element card structure, the three-lens validation table, and the Silent Review method). It includes concrete concept descriptions that stakeholders can react to. It also asks a closing question ('Want help preparing the specific Silent Review questions for each stakeholder group?'), which engages the user on validation goals. However, it doesn't explicitly ask upfront about the target audience, their validation goals, or what kind of feedback they want \u2014 it largely assumes a manufacturing/factory context and prescribes specific stakeholder groups rather than asking who the stakeholders are. The closing question partially compensates but the answer is more prescriptive than inquiry-driven regarding audience and feedback goals.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.7881112368083754, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.9099999999999999, - "passed": true, - "grade_count": 2 - } - ] - }, - { - "id": "method_6_scrappy_prototypes", - "name": "Method 6: Encourage scrappy constraint discovery", - "category": "method-guidance", - "overall_score": 0.9283994633082896, - "passed": true, - "time_seconds": 30.819407665985636, - "metric_scores": { - "latency": 0.7901983899248686, - "quality": 0.9975 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.7901983899248686, - "metric": "latency", - "passed": true, - "detail": "25.2s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 0.995, - "metric": "quality", - "passed": true, - "detail": "length 3015 chars (range 80-3000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be steers away from jumping to code and encourages a low-fidelity approach (paper sketches, wireframes, clickable mockups) to discover constraints cheaply before investing in development; asks what assumptions they want to test with the prototype\n", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "The answer strongly steers away from jumping to React code, explicitly stating 'No. Not yet.' It thoroughly encourages low-fidelity approaches including paper sketches, cardboard prototypes, poster mockups, and a Wizard of Oz whiteboard test. It clearly articulates specific assumptions to test (workers will look at it, readability, right information displayed, placement, update frequency) with a detailed table mapping assumptions to lo-fi methods. It also explains what lo-fi prototyping typically reveals and provides clear criteria for when to move to React.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.7901983899248686, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.9975, - "passed": true, - "grade_count": 2 - } - ] - }, - { - "id": "method_7_feasibility_testing", - "name": "Method 7: Guide technical feasibility testing", - "category": "method-guidance", - "overall_score": 0.7729166475695352, - "passed": true, - "time_seconds": 39.00543595798081, - "metric_scores": { - "latency": 0.7567499427086053, - "quality": 0.7809999999999999 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.7567499427086053, - "metric": "latency", - "passed": true, - "detail": "29.2s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 0.742, - "metric": "quality", - "passed": true, - "detail": "length 3774 chars (range 80-3000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be helps the user think through technical feasibility questions and what they need to prove with the high-fidelity prototype; asks about technical constraints, integration points, and what \"good enough\" looks like at this stage\n", - "score": 0.82, - "metric": "quality", - "passed": true, - "detail": "The answer thoroughly addresses technical feasibility by covering PLC protocols (OPC-UA, Modbus, EtherNet/IP), integration points (historian, SCADA, MES, edge gateway), and defines 'good enough' metrics (under 3 seconds latency, reliability over a full shift, network impact). It provides structured tables for what to validate, technical approaches to compare, and what to measure. It correctly emphasizes read-only safety boundaries and scoping to one production line. However, the criterion specifically asks the response to 'ask about' technical constraints, integration points, and what good enough looks like \u2014 meaning it should probe the user's specific situation with questions. The answer mostly prescribes rather than asks. It ends with one closing question but doesn't inquire about the user's specific PLC types, existing network topology, current protocols in use, what latency threshold they actually need, or what their plant's OT security policies are. A stronger response would have interspersed targeted questions to draw out the user's specific context before prescribing solutions.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.7567499427086053, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.7809999999999999, - "passed": true, - "grade_count": 2 - } - ] - }, - { - "id": "method_8_systematic_validation", - "name": "Method 8: Structure user testing for validation", - "category": "method-guidance", - "overall_score": 0.9088545674750801, - "passed": true, - "time_seconds": 42.811347832961474, - "metric_scores": { - "latency": 0.7265637024252404, - "quality": 1.0 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.7265637024252404, - "metric": "latency", - "passed": true, - "detail": "32.8s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 4622 chars (range 80-5000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be helps the user plan systematic user testing by addressing success criteria, test scenarios, observation methods, or feedback capture; includes questions or prompts that encourage the user to think about what they need to learn\n", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "The answer is exceptionally thorough in helping plan systematic user testing. It addresses success criteria (comprehension time <10s, accuracy >90%, unprompted usage patterns, trust metrics, handoff impact), defines multiple test scenarios (task-based testing, passive observation, shift handoff), provides detailed observation methods (structured observation tables, timing, behavior tracking), and includes robust feedback capture (specific interview questions with a helpful do/don't comparison). It also includes prompts that encourage reflection: writing down predictions before testing, interpreting results with clear decision paths, and considering environmental conditions. The closing question about observation recording sheets further encourages the user to think about what they need to learn.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.7265637024252404, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 1.0, - "passed": true, - "grade_count": 2 - } - ] - }, - { - "id": "method_9_continuous_optimization", - "name": "Method 9: Guide continuous optimization approach", - "category": "method-guidance", - "overall_score": 0.876388018172127, - "passed": true, - "time_seconds": 41.598102874995675, - "metric_scores": { - "latency": 0.6969640545163809, - "quality": 0.9661 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.6969640545163809, - "metric": "latency", - "passed": true, - "detail": "36.4s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 0.9322, - "metric": "quality", - "passed": true, - "detail": "length 5339 chars (range 80-5000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be addresses scaling considerations \u2014 acknowledges that what worked at one plant may not transfer directly; covers differences between sites, feedback loops, or metrics for ongoing optimization\n", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "The answer thoroughly addresses scaling considerations. It explicitly warns against copying Plant B directly ('Don't just copy Plant B to five plants'), details how sites differ across technical, user, process, and constraint dimensions, establishes feedback loops at multiple cadences (weekly, monthly, quarterly, annually), defines meaningful adoption metrics vs. vanity metrics, and includes anti-patterns like ignoring plant differences. The phased rollout approach directly embodies the principle that what works at one site may not transfer.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.6969640545163809, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.9661, - "passed": true, - "grade_count": 2 - } - ] - }, - { - "id": "hint_broad_direction_first", - "name": "Start with broad hints when user is stuck", - "category": "progressive-hints-and-navigation", - "overall_score": 0.71563184559992, - "passed": true, - "time_seconds": 24.96545470901765, - "metric_scores": { - "latency": 0.8468955367997599, - "quality": 0.65 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.8468955367997599, - "metric": "latency", - "passed": true, - "detail": "18.4s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 2006 chars (range 50-3000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be provides a broad directional hint or gentle starting point rather than jumping straight to a detailed step-by-step process; acknowledges the feeling of being lost and offers a manageable first move like looking for recurring words or surprising moments in the notes\n", - "score": 0.3, - "metric": "quality", - "passed": false, - "detail": "The answer does acknowledge the feeling of being lost ('Take a breath. You're not lost') and offers a first move, but it quickly jumps into a detailed, structured step-by-step process with tables, timelines, and multiple sequential instructions. The criterion asks for a 'broad directional hint or gentle starting point' rather than a detailed walkthrough. While the tone is supportive, the level of detail and prescriptiveness (5 observations, then second interview, then compare, with a time table) goes well beyond a gentle nudge. It also doesn't suggest simple starting points like 'look for recurring words or surprising moments' \u2014 instead it prescribes a specific methodology.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.8468955367997599, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.65, - "passed": false, - "grade_count": 2 - } - ] - }, - { - "id": "hint_escalation_on_repeated_confusion", - "name": "Escalate hints when user remains stuck", - "category": "progressive-hints-and-navigation", - "overall_score": 0.9282569254638574, - "passed": true, - "time_seconds": 27.4351705830195, - "metric_scores": { - "latency": 0.8347707763915726, - "quality": 0.975 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.8347707763915726, - "metric": "latency", - "passed": true, - "detail": "19.8s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 2543 chars (range 50-3000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be escalates to a more specific level of guidance \u2014 perhaps suggesting a concrete technique like looking for emotional reactions, workarounds people mentioned, or grouping by job role \u2014 while still letting the user do the actual synthesis work\n", - "score": 0.95, - "metric": "quality", - "passed": true, - "detail": "The answer excellently escalates guidance to a concrete, specific level. It provides multiple actionable techniques: (1) a three-column exercise (slows down / work around / wish existed), (2) a specific four-question comparison test (waiting, duplicating, unofficial tools, confusion), and (3) reframing from looking for 'same' to looking for 'similar frustration.' These are all concrete analytical lenses the user can apply. Crucially, it doesn't do the synthesis for the user \u2014 it hands them structured frameworks and asks them to apply these to their own data. The only minor note is the depth of scaffolding is so thorough it slightly edges toward hand-holding, but it still clearly leaves the actual pattern-finding work to the user.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.8347707763915726, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.975, - "passed": true, - "grade_count": 2 - } - ] - }, - { - "id": "backward_transition_accepted", - "name": "Accept backward transitions between methods", - "category": "progressive-hints-and-navigation", - "overall_score": 0.9358775611111518, - "passed": true, - "time_seconds": 29.525921416003257, - "metric_scores": { - "latency": 0.8076326833334557, - "quality": 1.0 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.8076326833334557, - "metric": "latency", - "passed": true, - "detail": "23.1s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 2794 chars (range 80-3000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be validates the decision to go backward, frames it as a normal and healthy part of the design thinking process, suggests returning to Method 2 (Design Research) to understand the night shift context, and helps identify what specific gaps to fill\n", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "The answer fully meets all aspects of the criterion: (1) validates going backward ('Yes, go back'), (2) frames it as normal and healthy ('This is exactly how Design Thinking is supposed to work. Backward movement isn't failure \u2014 it's the process catching a gap before you build the wrong thing'), (3) explicitly suggests returning to Method 2 (Design Research) scoped to the night shift context, and (4) provides extensive detail on specific gaps to fill including targeted research questions, common night shift differences to investigate, and a structured research plan.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.8076326833334557, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 1.0, - "passed": true, - "grade_count": 2 - } - ] - }, - { - "id": "transparent_method_shift", - "name": "Announce method shifts transparently", - "category": "progressive-hints-and-navigation", - "overall_score": 0.8712376880778012, - "passed": true, - "time_seconds": 28.37663266598247, - "metric_scores": { - "latency": 0.8637130642334038, - "quality": 0.875 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.8637130642334038, - "metric": "latency", - "passed": true, - "detail": "16.4s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 1846 chars (range 50-3000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be acknowledges the idea positively but is transparent about the method shift \u2014 notes they are currently in problem space (synthesis) and the idea jumps to solution space; asks whether they want to capture the idea and continue synthesis or deliberately shift to brainstorming\n", - "score": 0.75, - "metric": "quality", - "passed": true, - "detail": "The answer acknowledges the idea positively ('creative idea'), is transparent about the method shift (clearly explains they are in synthesis/problem space and the idea jumps to solution space), and suggests capturing the idea on a parking lot. However, it does not genuinely ask the user whether they want to capture and continue synthesis OR deliberately shift to brainstorming \u2014 instead it prescribes a single path (park it, go back to synthesis). The criterion requires asking the user to choose between those two options, giving them agency. The answer is directive rather than offering a choice, which partially misses the criterion.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.8637130642334038, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.875, - "passed": true, - "grade_count": 2 - } - ] - }, - { - "id": "no_multiple_choice_quizzes", - "name": "Avoid multiple-choice question lists", - "category": "progressive-hints-and-navigation", - "overall_score": 0.7275409180556177, - "passed": true, - "time_seconds": 21.962721332965884, - "metric_scores": { - "latency": 0.8826227541668534, - "quality": 0.65 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.8826227541668534, - "metric": "latency", - "passed": true, - "detail": "14.1s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 1747 chars (range 50-3000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be does NOT respond with a numbered list of options like a quiz (e.g. \"1. Move to synthesis 2. Do more interviews 3. Revisit scope\"); instead offers a conversational observation about what seems ready and asks one focused question\n", - "score": 0.3, - "metric": "quality", - "passed": false, - "detail": "The answer does not present a simple numbered quiz-style list of options like '1. Move to synthesis 2. Do more interviews 3. Revisit scope'. However, it still presents multiple discrete options in a structured, decision-tree format (Method 2 vs Method 3 vs 'a third possibility') with headers and a comparison table, which is closer to a quiz/decision matrix than a conversational observation. It does end with a focused question ('Who were your 5 interviewees?'), which partially meets the criterion. But the overall tone is structured and prescriptive rather than conversational \u2014 it doesn't offer a warm, natural observation about what seems ready. It reads more like a diagnostic flowchart than a conversational coaching response.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.8826227541668534, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.65, - "passed": false, - "grade_count": 2 - } - ] - }, - { - "id": "no_unsolicited_method_change", - "name": "Do not change method focus without announcing it", - "category": "progressive-hints-and-navigation", - "overall_score": 0.7077826891194693, - "passed": true, - "time_seconds": 29.572733915993012, - "metric_scores": { - "latency": 0.8233480673584078, - "quality": 0.65 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.8233480673584078, - "metric": "latency", - "passed": true, - "detail": "21.2s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 2614 chars (range 50-3000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be stays within Method 3 (Input Synthesis) and helps the user evaluate the significance of this finding as a synthesis pattern; does NOT silently jump to brainstorming solutions for the texting workaround\n", - "score": 0.3, - "metric": "quality", - "passed": false, - "detail": "The answer starts well by helping the user assess the significance of the workaround as a synthesis pattern. However, it significantly overreaches Method 3 (Input Synthesis) boundaries. The 'What this workaround might be telling you' section begins framing design directions ('much richer design direction than we need a dashboard'), discusses solution characteristics (visual, asynchronous, mobile, fast), and essentially starts brainstorming solution attributes. The 'Record it as an insight' section frames the finding in terms of 'what you build,' which is solution-oriented thinking beyond synthesis. While it doesn't explicitly propose a specific product solution, it clearly drifts from pure synthesis into solution-space thinking, which the criterion says it should NOT do.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.8233480673584078, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.65, - "passed": false, - "grade_count": 2 - } - ] - }, - { - "id": "session_resumption", - "name": "Resume session with state context", - "category": "progressive-hints-and-navigation", - "overall_score": 0.9465651578721008, - "passed": true, - "time_seconds": 18.873716333997436, - "metric_scores": { - "latency": 0.8896954736163025, - "quality": 0.975 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.8896954736163025, - "metric": "latency", - "passed": true, - "detail": "13.2s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 1317 chars (range 80-3000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be acknowledges the returning user, references Method 2 (Design Research), and asks about or summarizes where they left off to re-establish context before continuing coaching\n", - "score": 0.95, - "metric": "quality", - "passed": true, - "detail": "The answer clearly acknowledges the returning user ('Welcome back'), explicitly references Method 2 and its phases (design research including environmental observation, user interviews, constraint validation, unmet need exploration), and thoroughly asks about where they left off to re-establish context before continuing coaching. The structured questions about research progress, findings, and practical status are all aimed at understanding the current state. The only minor gap is that it could have been slightly warmer in the greeting, but functionally it meets all aspects of the criterion very well.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.8896954736163025, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.975, - "passed": true, - "grade_count": 2 - } - ] - }, - { - "id": "init_asks_for_project_slug", - "name": "Ask for project slug during initialization", - "category": "session-phases", - "overall_score": 0.6442423405083698, - "passed": true, - "time_seconds": 27.134625458042137, - "metric_scores": { - "latency": 0.8327270215251095, - "quality": 0.55 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.8327270215251095, - "metric": "latency", - "passed": true, - "detail": "20.1s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 2654 chars (range 50-3000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be asks the user for a project slug (a kebab-case identifier) or proposes one, and begins gathering context about the user's role, team, and which method they want to start with\n", - "score": 0.1, - "metric": "quality", - "passed": false, - "detail": "The answer does not ask for or propose a project slug (kebab-case identifier) for the project. It also does not gather context about the user's role or team in a structured way. Instead, it jumps directly into prescriptive methodology advice about Method 1: Scope Conversations, stakeholder mapping, and interview questions. While it asks 'Who do you have access to for conversations this week?' at the end, this is about stakeholder access rather than gathering context about the user's own role and team. The criterion specifically requires asking for/proposing a project slug and beginning to gather context about the user's role, team, and preferred starting method \u2014 none of which are addressed.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.8327270215251095, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.55, - "passed": false, - "grade_count": 2 - } - ] - }, - { - "id": "init_clarifies_context", - "name": "Gather role, team, and method focus during init", - "category": "session-phases", - "overall_score": 0.8243579225693894, - "passed": true, - "time_seconds": 33.98444858397124, - "metric_scores": { - "latency": 0.7730737677081683, - "quality": 0.85 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.7730737677081683, - "metric": "latency", - "passed": true, - "detail": "27.2s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 793 chars (range 50-3000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be acknowledges the user's project context, then asks about the user's role, team composition, which design thinking method to focus on, session goals, or time constraints \u2014 covering at least one of these initialization items\n", - "score": 0.7, - "metric": "quality", - "passed": true, - "detail": "The answer acknowledges the user's project context ('customer portal redesign') and references previous session history. It asks initialization-style questions about the project's origin and stakeholder/sponsor ('Who's asking for it?'), which partially covers team composition and session goals. However, it doesn't explicitly ask about the user's role, time constraints, which design thinking method to focus on, or specific session goals. The questions asked are more about project background than the initialization items listed in the criterion, though 'Who's asking for it?' touches on team composition/stakeholder context.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.7730737677081683, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.85, - "passed": true, - "grade_count": 2 - } - ] - }, - { - "id": "init_defaults_to_method_1", - "name": "Default to Method 1 for new projects", - "category": "session-phases", - "overall_score": 0.597602826041758, - "passed": true, - "time_seconds": 47.18088508298388, - "metric_scores": { - "latency": 0.692808478125274, - "quality": 0.55 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.692808478125274, - "metric": "latency", - "passed": true, - "detail": "36.9s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 1911 chars (range 50-3000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be for a brand-new project with no prior design thinking work, suggests starting at the beginning of the process (problem space / early methods); acknowledges the team composition and time constraints and begins transitioning to coaching\n", - "score": 0.1, - "metric": "quality", - "passed": false, - "detail": "The answer fundamentally fails the criterion. The user explicitly stated 'We haven't done any design thinking on this yet,' but the answer fabricates prior progress ('Method 1 is complete,' fake stakeholder findings, invented statistics like '30% of paper failure forms get lost'). Instead of starting at the beginning of the design thinking process as the criterion requires, it hallucinates a history that doesn't exist and jumps to Method 2. While it does eventually propose a structured hour-long session and loosely acknowledges the team, the core failure \u2014 contradicting the user's stated reality and not starting from the problem space \u2014 means it misses the primary requirements of the criterion.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.692808478125274, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.55, - "passed": false, - "grade_count": 2 - } - ] - }, - { - "id": "active_coaching_open_ended_questions", - "name": "Ask targeted, open-ended questions during coaching", - "category": "session-phases", - "overall_score": 0.6808790335638656, - "passed": true, - "time_seconds": 29.90252399997553, - "metric_scores": { - "latency": 0.8426371006915967, - "quality": 0.6 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.8426371006915967, - "metric": "latency", - "passed": true, - "detail": "18.9s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 1567 chars (range 50-3000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be asks open-ended questions to help the user discover the real problem behind the solution request (e.g. \"what happens today when a technician finds a failure?\"), rather than accepting \"build a mobile app\" at face value\n", - "score": 0.2, - "metric": "quality", - "passed": false, - "detail": "The answer recognizes that 'build a mobile app' is a 'frozen request' and reframes the problem, which shows awareness. However, it does NOT ask open-ended discovery questions to help the user uncover the real problem themselves. Instead, it presents pre-determined findings as if the discovery already happened ('your team already found...'), tells the user what the reframed problem is, and then offers a closed binary choice (Option A or B). The criterion specifically calls for open-ended questions like 'what happens today when a technician finds a failure?' \u2014 none of that exploratory questioning appears. The answer does the opposite of the criterion: it provides answers rather than asking questions to guide discovery.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.8426371006915967, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.6, - "passed": false, - "grade_count": 2 - } - ] - }, - { - "id": "active_coaching_periodic_summary", - "name": "Summarize progress and check direction", - "category": "session-phases", - "overall_score": 0.8382431398139386, - "passed": true, - "time_seconds": 23.100201666995417, - "metric_scores": { - "latency": 0.864729419441816, - "quality": 0.825 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.864729419441816, - "metric": "latency", - "passed": true, - "detail": "16.2s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 1978 chars (range 80-3000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be summarizes or reflects back the key findings, then asks whether the user wants to go deeper into any of these areas, broaden scope, or move on to the next step\n", - "score": 0.65, - "metric": "quality", - "passed": true, - "detail": "The answer does reflect back the key findings by listing the pain points and showing how they connect in a dependency chain. However, it doesn't cleanly summarize them as a discrete step before moving forward \u2014 instead it immediately reframes and analyzes them. It does ask a forward-looking question at the end ('How many field technicians can you get access to?'), but it doesn't explicitly ask whether the user wants to go deeper into any of the identified areas, broaden scope, or move on. It essentially assumes the user wants to move to Method 2 and prescribes next steps rather than offering the user a choice of directions.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.864729419441816, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.825, - "passed": true, - "grade_count": 2 - } - ] - }, - { - "id": "method_transition_recap_and_confirm", - "name": "Recap accomplishments and confirm method change", - "category": "session-phases", - "overall_score": 0.9309120305555147, - "passed": true, - "time_seconds": 29.37909458298236, - "metric_scores": { - "latency": 0.8427360916665445, - "quality": 0.975 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.8427360916665445, - "metric": "latency", - "passed": true, - "detail": "18.9s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 1606 chars (range 80-3000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be explicitly confirms the method transition, briefly recaps key accomplishments from Method 1 (scope conversations), and suggests the next method (Method 2: Design Research) with a clear connection to the previous work\n", - "score": 0.95, - "metric": "quality", - "passed": true, - "detail": "The answer explicitly confirms the transition ('your coaching state already confirms Method 1 is complete and you're at Method 2'), recaps key accomplishments from Method 1 (the reframing from 'Build a mobile app for failure reports' to 'Delayed visibility into equipment health', plus specific findings like 30% forms lost, end-of-shift batching, no photo support, irrelevant fields), and clearly suggests Method 2: Design Research with a detailed connection to Method 1 work (a table mapping each Method 1 finding to what research needs to uncover, plus three concrete activities). The only minor gap is that it doesn't explicitly say 'Method 1: Scope Conversations is complete' in those exact words, but the confirmation is clearly implied and stated.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.8427360916665445, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.975, - "passed": true, - "grade_count": 2 - } - ] - }, - { - "id": "session_closure_summary", - "name": "Summarize session and suggest next steps on closure", - "category": "session-phases", - "overall_score": 0.7403985196777891, - "passed": true, - "time_seconds": 27.829361541953404, - "metric_scores": { - "latency": 0.8211955590333673, - "quality": 0.7 - }, - "error": null, - "grades": [ - { - "criterion": "completion time should be under", - "score": 0.8211955590333673, - "metric": "latency", - "passed": true, - "detail": "21.5s of 120.0s threshold", - "layer": "deterministic", - "skipped": false, - "stage": 1, - "stage_name": "the agent processes the request" - }, - { - "criterion": "response length should be", - "score": 1.0, - "metric": "quality", - "passed": true, - "detail": "length 538 chars (range 80-3000)", - "layer": "deterministic", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - }, - { - "criterion": "the answer should be summarizes the session journey, highlights key decisions or artifacts, mentions open questions or follow-up work, and suggests how to pick up in a future session including which method to revisit\n", - "score": 0.4, - "metric": "quality", - "passed": false, - "detail": "The answer provides a partial session summary and mentions the current method to revisit (Method 1 - Scope Conversations), which satisfies the 'suggests how to pick up' and 'which method to revisit' parts. However, it falls short on several criterion elements: it does not summarize the actual session journey (what was discussed, what steps were taken), does not highlight key decisions or artifacts produced during the session, and does not mention open questions or follow-up work beyond generic preparation items. The summary feels more like a project status stub than a genuine reflection of a session's activities and outcomes.", - "layer": "ai_judged", - "skipped": false, - "stage": 2, - "stage_name": "the agent responds" - } - ], - "stages": [ - { - "stage": 1, - "name": "the agent processes the request", - "score": 0.8211955590333673, - "passed": true, - "grade_count": 1 - }, - { - "stage": 2, - "name": "the agent responds", - "score": 0.7, - "passed": false, - "grade_count": 2 - } - ] - } - ] -} \ No newline at end of file