A convincing answer can hide failed work

An agent may write a convincing explanation after changing the wrong file or skipping a required step. Evaluate both the actual outcome and tool-use trajectory. Anthropic's engineering guide distinguishes code-based, model-based, and human graders, with the choice depending on the task.

Machine checks suit specific, observable conditions. Writing quality needs explicit criteria and sometimes human judgment. Model graders should be calibrated against human examples. A single overall score can obscure differences in accuracy, boundary compliance, and presentation quality.

Test everyday situations

Collect requests from the team that will use the system. A support evaluation should include incomplete requests, duplicate customers, missing information, and temporarily unavailable tools alongside easy cases. Write expected outcomes and acceptable failure behavior before running the evaluation.

Use a stable, repeatable environment. Changing test data or unwritten expectations weakens version comparisons. Separate development examples from final evaluation cases so instructions are not optimized only for a familiar set of questions.

Measure cost and completion time alongside correctness. Repeated unnecessary attempts can make a successful agent unsuitable for daily use. Group failures into causes such as weak retrieval, incorrect tool selection, and missing information to guide the next improvement.

An illustrative agent evaluation case

This teaching example sketches an evaluation case rather than a configuration for a specific framework. Implement its measurable checks in the actual test environment.

Input, expected outcome, and grading checks
task:
  id: policy-answer-01
  input: "Summarize the approved leave policy."
  expected_outcome:
    source_cited: true
    records_changed: false
  graders:
    - citation_support
    - no_write_tool_calls
    - final_state_check

Success is more than the final reply: citations must support the answer, and the agent must not call a data-changing tool.

Keep results traceable

Rerun the fixed suite before significant changes and add cases from real failures over time. Record model and tool versions with each result. This makes evaluation an ongoing product maintenance practice rather than a demonstration performed once.

Practical explanations and recommendations are Liyan Knowledge editorial analysis.Sources: Anthropic Engineering — Demystifying Evals for AI Agents

This Liyan Knowledge article is an editorial synthesis based on the original source.View original source