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Summary

Evaluation tells you whether the agent is good enough for a task. Observability tells you why it passed or failed. Production systems need both, because scores without traces are hard to improve and traces without metrics are hard to prioritize.

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Why It Matters

Agent systems are probabilistic and multi-step. That makes them harder to judge than deterministic software.
  • A correct answer may depend on search, tools, or file state.
  • The same task can fail for very different reasons.
  • A prompt or model change can improve one capability while silently harming another.
Teams therefore need two loops:
  • an evaluation loop for measuring capability
  • a diagnostic loop for explaining behavior

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Mental Model

Think in three layers.
  • offline evaluation: benchmark-style checks run on known tasks to compare prompts, models, tools, and policies.
  • online evaluation: production signals such as success rate, latency, escalation rate, retries, or human overrides.
  • observability: traces, tool logs, state transitions, and artifacts that show what the system actually did.
Different task types need different metrics.
  • Tool use often needs structured correctness checks such as function and parameter matching.
  • General assistant tasks often need answer-level correctness plus task-level completion.
  • Data generation or synthesis tasks may need comparative review, judge models, or human verification.

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Architecture Diagram

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Tool Landscape

The imported reference material highlights three useful evaluation shapes:
  • benchmark-style tool-use evaluation, where structured matching checks whether the agent selected the right function and arguments
  • general-assistant evaluation, where tasks require multi-step reasoning and broader success judgments
  • generation-quality evaluation, where relative comparison or human review is often more useful than one exact metric
Observability should remain structured from the start.
  • Keep full tool inputs and outputs.
  • Preserve failure records rather than collapsing them into generic errors.
  • Track step order, retries, and state changes.
  • Keep traces readable by both humans and machines.
That is what turns a black-box failure into an actionable bug.

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Tradeoffs

  • Offline benchmarks are useful, but they can overfit the system to lab tasks that are cleaner than production reality.
  • Online metrics reflect real usage, but they lag and are noisy without good segmentation.
  • Judge-model evaluation scales well, but it still needs human calibration.
  • Rich traces improve diagnosis, but they create storage, privacy, and review overhead.
Useful operating defaults:
  • evaluate the capability you are actually changing
  • keep traces for both failed and successful runs
  • review failure modes before rewriting prompts
  • do not ship “tool failed” as the only explanation developers can see

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Citations

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Reading Extensions

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Update Log

  • 2026-04-21: Initial repo-native draft based on imported reference material and lab rewrite rules.