How Do You Evaluate an AI Agent? Tasks, Trajectories, Reliability, and Cost

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Evaluating an AI agent requires more than checking its final answer. Strong evals measure outcomes, inspect trajectories, repeat trials, test safety boundaries, and track the cost of getting reliable work done.

Evaluating an AI agent means testing whether it completes representative tasks reliably, follows acceptable paths, respects its boundaries, and delivers enough value for its cost. A final answer alone is not sufficient because an agent can reach a plausible outcome through unsafe actions, unsupported claims, excessive retries, or luck.

Agent evaluation is difficult because the system acts over time. The model chooses tools, observes changing environments, and may take a different route in every run. Effective evals therefore measure outcomes and trajectories across repeated trials.

Anthropic’s guide to demystifying agent evaluations provides a useful set of terms:…

Keeping these concepts separate prevents a common mistake: treating one successful demonstration as evidence that the agent will perform reliably.

A useful evaluation task describes the initial state, available tools, constraints, expected outcome, and important prohibited behavior. The test should resemble real work without revealing a single scripted path.

For a support agent, “resolve the ticket” is too vague. A stronger task specifies the customer’s record, applicable policy, allowed remedies, required communication, and which changes must appear in the system of record. It can then be graded objectively.

These layers should remain separate in reporting. Combining them into one average can hide unacceptable behavior—for example, a high task-success score that masks frequent unauthorized tool attempts. Teams can define hard safety gates first, then compare outcome quality and efficiency among runs that pass those gates.

Did the agent accomplish the goal? Outcome graders can check exact state changes, required fields, test results, factual accuracy, citation support, or rubric-based quality.

Where possible, use the environment’s final state rather than the agent’s own claim. A coding agent has not succeeded because it says the bug is fixed; the relevant tests and behavior must confirm it.

Did the agent take a sound path? Trajectory evaluation can detect unnecessary data access, prohibited tools, repeated failures, skipped verification, fabricated observations, or approval violations.

Not every deviation from a reference path is an error. Agents can find valid solutions that evaluators did not anticipate.