feat(agents): add enablement dimension to Experiment Designer for code-with MVEs

[eedorenko]

In code-with engagements (ISE or similar), MVEs serve a dual purpose: validate feasibility and enable the customer to own the outcome independently. The Experiment Designer agent previously focused entirely on the validation side — hypothesis formation, vetting, and experiment design — without prompting the user to think about whether the customer would leave the engagement able to replicate the work.

This PR adds enablement-aware guidance throughout the agent's phases and companion instructions, drawn from a real ISE engagement designing an MVE for Azure Confidential Computing migration.

Changes

Agent: Experiment Designer (experiment-designer.agent.md)

• Phase 1 (Discovery): added two probing questions — whether this is a code-with engagement and what the customer's current knowledge level is. Added guidance that code-with MVEs should reflect a dual purpose in the problem statement.
• Phase 1 (Context tracking): added enablement goal as a captured field in context.md.
• Phase 3 (Red Flag Checklist): added Show without teach — flags engagements where the customer watches but does not participate in building.
• Phase 4 (Experiment Design): added Enablement Design section for code-with engagements covering pairing structure, ownership progression (ISE leads → joint → customer leads), knowledge transfer checkpoints, and enablement as a measurable success criterion.
• Phase 5 (MVE Plan): added enablement plan to the mve-plan.md contents list.
• Coaching Style: reinforced that the customer leaving unable to replicate the outcome is a failure mode even if all hypotheses are validated.

Instructions: Experiment Designer (experiment-designer.instructions.md)

• Added MVE as Enablement section under "What is an MVE" defining the dual-purpose model with five principles: joint work from scratch, full-stack understanding, ownership progression, enablement as measurable outcome, and embedded knowledge transfer.
• Added Show without teach to the Red Flags list — a demo disguised as an experiment.
• Added Customer as passive observer to Common Pitfalls — designing the experiment so the customer watches instead of drives.

Notes

The enablement insight emerged from designing an MVE for a real customer migrating AI inference workloads from AWS TEVM to Azure Confidential Computing. The customer's engineering team needed to leave the engagement owning the full Azure CC stack (AKS, attestation, SKR, GPU CC mode), not just seeing a validated architecture. Prior ISE research was preparation, not scope reduction — all work was done jointly from scratch.

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 87.16%. Comparing base (57ea279) to head (ed24cfb).

Additional details and impacted files

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- Coverage   87.44%   87.16%   -0.28%     
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  Files          68       67       -1     
  Lines       10335    10067     -268     
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- Hits         9037     8775     -262     
+ Misses       1298     1292       -6     

Flag	Coverage Δ
pester	84.79% <ø> (-0.02%)	⬇️

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see 3 files with indirect coverage changes

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[eedorenko]

@mattdot

[bindsi]

Good additions to the agent/instructions to give more accuracy and constraints. Thanks

[katriendg]

Thanks, great additions. Only comments left are around generalizing more. Taking into account this could be used by any type of teams collaborating together.

[katriendg]

Thank you @eedorenko, this looks good to me other than the committed json files which I believe should be removed, they don't fit this PR.
Once that is done, all good for me.

[katriendg]

@mattdot could you please re-review this one? Thanks.