How to Build Effective Evals for AI Agents

Agents run multi-step processes: they reason, choose tools, act, observe results, and loop. A single early mistake changes the state that every later step uses, so errors compound. That means a simple pass/fail on the final answer hides where the system went wrong. Design evals that report which layer failed so engineers can investigate the right component.

• Reasoning: Did the agent understand the task, break it into the right sub-steps, and pick the correct order? Example failure: trying to book a flight before checking availability.
• Action: Did the agent pick the right tool and call it with correct arguments at the right time? Example failure: passing an unrecognized airport code to a flight-search API.
• Overall execution: Was the task completed and with reasonable efficiency? Example failure: completing the booking but repeating the same search multiple times.

Sourcing tasks: start small and balanced

You don't need hundreds of cases to begin. Convert the manual checks you already run into repeatable eval tasks: common workflows, release checks, and known edge cases. Keep the set balanced by including both cases where an action is expected and cases where it should be avoided (for example, queries that should use search versus ones that can be answered directly). A small, well-chosen set catches meaningful regressions earlier and is easier to maintain.

Choosing graders and what they should check

Eval design should include graders at multiple points:

• Reasoning graders: score the agent's decomposition and chosen plan.
• Action graders: verify tool selection, call timing, and argument correctness.
• Outcome graders: judge whether the final result solves the user's problem and whether the approach is reasonable.

Frontier models often find valid solutions you didn't expect. Avoid rigid graders that enforce a single step sequence. Prefer outcome-focused rubrics that accept alternative valid trajectories while still detecting incorrect or unsafe behavior.

Make the harness minimize noise so results reflect real behavior changes. Use repeatable inputs, isolate external variability where possible, and prefer deterministic or controlled tool mocks for tests that must be stable. Capture intermediate steps and structured traces so graders can inspect where the trajectory diverged.

Record eval outcomes across releases and component changes (system prompt edits, tool descriptions, model versions). A consistent metric history helps you map regressions to specific changes instead of guessing. Small, repeatable tasks make it easier to detect when a modification changed behavior and to validate fixes.

• Turn three to ten high-value manual checks into formal eval tasks. Include both expected and avoided behaviors.
• Instrument agents to record plans, tool calls, and outputs so graders can inspect layers.

• Run evals on every change to prompt, tool description, or model version and track trends over time.