agent-instability-notes

Small, practical patterns and metrics for detecting early instability in multi-turn agents – using only traces, latency and simple structure, without any framework lock-in.

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🎯 What this repository is about

This repo is a technical notebook + small experiments collection for people who:

• work with multi-turn agents in production or research,
• already have traces / logs / latency data, and
• want better intuition and tools for spotting early signs of instability before they turn into visible failures.

You do not need to adopt any specific framework to use this repo. All examples are based on:

• JSONL traces (one event per line),
• simple structural assumptions (trace ID / span ID / timestamps),
• a few small Python utilities and notebooks.

Frameworks such as Langfuse, Arize, LangChain, LlamaIndex, Semantic Kernel and others are treated as examples, not dependencies.

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👤 Intended audience

This repo is aimed at:

• observability / tracing engineers for LLM systems,

• people building or operating multi-turn agents / tools / workflows,

• senior practitioners who “watch traces every day” but want a more systematic view of:

  • early instability signals
  • correction & recovery loops
  • long-horizon behavior.

If you already ship agents but feel that “the theory of stability” is scattered across your notes and dashboards, this repo is for you.

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🧩 Repository structure

High-level layout:

agent-instability-notes/
  README.md
  LICENSE
  requirements.txt

  concepts/
    00_overview.md
    early-instability-signals.md
    correction-and-recovery-loop.md
    trace-tree-divergence.md
    nested-tool-inconsistency.md
    instability-patterns_catalog.md

  metrics/
    _shared_definitions.md
    relative-latency-gap.md
    recovery-turn-distance.md
    post-correction-relapse-rate.md
    failover-frequency.md
    session-closure-profile.md

  examples/
    README.md
    long-horizon-toy-agent.md
    tool-chain-instability.md
    workflow-looping.md
    notebooks/
      01_inspect_trace_tree.ipynb
      02_early_instability_metrics.ipynb
      03_visualize_trace_divergence.ipynb
    synthetic_traces/
      README.md
      simple_correction_loop.jsonl
      noisy_mixed_sessions.jsonl
      generate_synthetic_traces.py

  scripts/
    compute_metrics_from_jsonl.py
    trace_tree_sanity_checks.py

  data/
    sample_configs/
      latency_thresholds.yaml
      instability_heuristics.yaml

  .github/
    workflows/
      lint-and-tests.yml

concepts/

Short, self-contained notes on:

• what we mean by early instability signals in multi-turn agents,
• how to think about correction and recovery loops,
• structural patterns like trace tree divergence and nested tool-call inconsistency,
• a small catalog of instability patterns seen in practice.

The goal is to give you language and shapes to describe what you already see in traces.

metrics/

Definitions and sketches for instability-focused metrics, such as:

• relative-latency-gap – relative latency differences across comparable spans,
• recovery-turn-distance – how many turns it takes for a session to behave “healthy” again,
• post-correction-relapse-rate – how often sessions relapse after an apparent correction,
• failover-frequency – how often flows fall back to alternative paths,
• session-closure-profile – how sessions actually end (natural completion, forced stop, abandonment, etc.).

Each metric doc focuses on:

• a plain-language definition,
• when the metric is useful,
• how to compute it from JSONL traces,
• caveats and interpretation notes.

examples/

Concrete scenarios and artifacts:

• narrative examples (Markdown) for:

  • long-horizon toy agents,
  • tool-chain instability,
  • workflow looping and recovery;

• notebooks for:

  • inspecting and visualizing trace trees,
  • computing instability metrics on synthetic traces,
  • plotting divergence and latency gaps;

• synthetic trace files:

  • a small “correction loop” trace,
  • a noisier mixed set of sessions,
  • a generator script so you can create your own.

scripts/

Lightweight utilities, for example:

• compute_metrics_from_jsonl.py → load a trace file and compute a handful of metrics defined under metrics/.

• trace_tree_sanity_checks.py → check basic properties such as:

  • monotonic timestamps per trace,
  • uniqueness of IDs,
  • simple structural invariants.

These scripts are intended as reference helpers, not as a production SDK.

data/sample_configs/

A small collection of example configuration files, such as:

• latency_thresholds.yaml – example thresholds for flagging suspicious latency gaps,
• instability_heuristics.yaml – rule-of-thumb patterns for defining “instability episodes”.

You’re expected to adapt these values to your own system and domain.

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⚙️ Getting started

1. Install dependencies

Create a virtual environment (recommended) and install:

pip install -r requirements.txt

Dependencies are kept minimal:

• pandas, numpy for data handling
• matplotlib for basic visualizations
• jupyterlab for notebooks
• small utility libs only when needed

No agent framework is required.

2. Open the notebooks

The quickest way to get a feel for the repo:

jupyter lab

Then start with:

• examples/notebooks/01_inspect_trace_tree.ipynb
• examples/notebooks/02_early_instability_metrics.ipynb

Use the synthetic traces under examples/synthetic_traces/ as initial input.

3. Run a simple CLI metric pass

Once you have your own JSONL traces, you can plug them into the CLI script:

python scripts/compute_metrics_from_jsonl.py \
  --file path/to/your_traces.jsonl

(Exact options and output format are documented inside the script.)

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🤝 How to use this repo in your own work

This repository is meant to be:

• a reference shelf for patterns and metrics around agent instability,

• a sandbox for your own experiments,

• a translation layer between raw traces and the questions you actually care about:

  • “Where do things start to wobble?”
  • “How long does it take to recover once they do?”
  • “When we fix something, how often does it quietly break again later?”

You’re encouraged to:

• fork and adapt the notebooks and scripts,
• plug the metrics into your own dashboards,
• add more patterns and examples that you see in the wild.

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🔗 Related Project: Phase Loop Dynamics (PLD)

This repository is a framework-agnostic notebook for understanding instability in multi-turn agents.
If you want the formal governance model, event lifecycle, and a full behavioral contract for
agent execution stability, the parent conceptual project is:

👉 PLD — Phase Loop Dynamics
https://github.com/kiyoshisasano/agent-pld-metrics

PLD provides:

• a formal Drift → Repair → Reentry → Continue lifecycle,
• a runtime reference implementation,
• schema-level definitions, metrics, and a complete taxonomy,
• end-to-end examples of multi-turn stability governance.

While this repository (agent-instability-notes) stands alone,
it also serves as a practical companion to PLD —
focused on practitioner vocabulary, early-instability signals,
and empirical metrics derived from traces.

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📄 License & contributions

License and contribution guidelines are intentionally lightweight and will be documented in:

• LICENSE

For now, feel free to open issues or pull requests if you have:

• new instability patterns,
• better visualizations,
• corrections or clarifications to the definitions.

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