Agent Evaluations

Quick Take

In brief: Agent evaluations test whether an agent meets defined task and safety criteria.
Useful for: Comparing versions and finding failures in tool-using workflows.
Main limit: Results depend on the test cases, environment, and graders.

Overview

A coding agent may be asked to fix a bug in a small repository. An evaluation checks whether the patch meets requirements, passes relevant tests, and stays within allowed actions.

Core Definition

Agent evaluation is the structured assessment of an agent’s observable behavior and task results. It can include final outputs, tool calls, policy compliance, and resource use. It does not require access to private model reasoning.

Background and Evolution

Traditional software tests often check deterministic inputs and outputs. Agent evaluations also need to account for changing model responses, tool use, and task completion. Teams combine automated checks with human review when criteria require judgment.

How It Works

Define representative tasks and constraints.
Run the agent in a controlled, recorded environment.
Check outputs and observable actions against a rubric.
Repeat runs when variability matters.
Review failures and compare results across versions.

A grader can be a test script, a person, or a model. Each type has different error risks.

Applications in Software Development and Testing

Evaluate bug fixes, test generation, repository questions, and tool permissions. Keep task inputs, code revisions, environment settings, and grader versions so the comparison can be reproduced.

Strengths and Limitations

Evaluations can reveal regressions and compare trade-offs across versions. A small test set may miss real tasks. Model-based graders may disagree with people or favor certain answer styles. Passing an evaluation does not prove production reliability.

Comparison with Similar Terms

Agent evaluation covers a set of tasks and criteria. A grader applies one or more criteria to a result. Unit tests check defined program behavior. Trace grading checks recorded steps in an agent run.

Best Practices

Use realistic tasks and clear pass criteria. Include negative and edge cases. Keep evaluation data separate from training data. Calibrate model graders against human review and inspect failures.

Common Pitfalls

Do not rely on one score or one run. Do not compare versions with different tools or environments without recording the difference. Do not treat a model grader as ground truth.

FAQ

Can agent evaluations prove an agent is safe?

No. They provide evidence for the tested tasks and conditions. Other tasks and environments can expose different failures.