A Practical Guide to Creating Strong Evaluations for AI Agents

If you build or maintain AI agents, you've probably seen it happen: after a prompt tweak, a tool description update, or a model version bump, the agent's behavior shifts — and nobody can say exactly why. This guide is for developers and technical teams who want to stop guessing. You'll learn how agent evals differ from standard LLM testing, how to write good eval tasks, how to pick the right graders, and how to build a harness that gives you trustworthy, repeatable results.

Why Evaluating Agents Is Harder Than Evaluating a Single LLM Call

Testing a single prompt is straightforward: you send one input, get one response, and a grader checks it against an expected answer. Agents don't work that way. An agent reasons through a task, selects a tool, executes it, looks at the outcome, and loops back — sometimes for dozens of iterations. Each step can fail on its own, and a mistake at the start corrupts the context every later step builds on. Errors stack up instead of staying contained.

Because of this compounding effect, it helps to think of agent failures in three layers rather than a simple pass or fail:

• Reasoning — did the agent choose a sensible approach?
• Action — did it use its tools correctly?
• Execution — did it complete the whole task efficiently?

Consider a travel-booking agent. It might fail by ordering its steps badly, by calling a tool with wrong parameters, or by technically succeeding but wasting calls along the way. A useful eval tells you which layer broke, not just that something did.

Frontier models add another wrinkle: given enough freedom, an agent may find a valid solution nobody anticipated when the task was written. A rigid grader would score that as a failure even though the agent solved the user's problem — possibly better than the scripted path. Graders should judge the outcome and whether the approach was reasonable, not demand one exact sequence of steps.

Sourcing and Writing Your First Eval Tasks

You don't need hundreds of test cases to get started. A small, focused set often catches meaningful regressions early, and it's much easier to convert clear requirements into test cases before your system grows complicated.

The quickest source of eval tasks is the checking you already do by hand: routine workflows, known edge cases, and the scenarios you verify before each release. Turn those into repeatable tasks instead of re-testing them from memory every time.

Keep the set balanced, too. Mix in cases where an action shouldn't happen alongside cases where it should. A search tool evaluation, for instance, should include both queries that genuinely need search and questions answerable without it. That way you're measuring whether the agent makes good decisions — not just whether it repeats a habit.

What makes a task well-written?

A good eval task has clear, objective success criteria: two people reviewing the same output should reach the same verdict. If the instructions are vague, the grader ends up measuring the ambiguity rather than the agent.

Before adding a task, confirm the instructions contain everything required to solve it. If your grader assumes details the task never provided, failures may reflect poor task design, not a bad agent.

Finally, write a reference solution. If a strong agent keeps failing, first verify that the task is actually solvable and that the grader can recognize a correct result. This quick sanity check prevents a lot of misleading data.

Choosing the Right Grader for Each Layer

Different aspects of agent behavior call for different evaluation methods:

• Tool selection and arguments: use deterministic or code-based checks right where the call happens.
• Plan quality: review the full trace when the order of decisions matters.
• Task completion: verify the final state directly with code, database queries, or system assertions — don't trust the agent's own summary.
• Open-ended outputs: use a model-based grader when there's no single right answer.
• Ambiguous or complex cases: fall back to human review when automation can't judge reliably.

One general rule: avoid grading the exact sequence of steps unless the order genuinely matters. An agent may take a different route and still reach the right destination. By default, grade outcomes; enforce specific steps only when they're truly required.

Building a Harness You Can Trust

Your eval is only as reliable as its environment. Every run should start from a clean, isolated state — leftover files, cached data, or shared conversation history can skew results in either direction.

Use partial credit rather than pure pass/fail. An agent that correctly diagnoses a problem and verifies the customer but misses the refund step is clearly better than one that misunderstood the request entirely. Binary scoring hides that difference.

Non-determinism matters too. Agents rarely produce identical results twice, so a single run can mislead. Two useful metrics here are the pass rate across repeated runs (how often the agent succeeds) and a pass-fail average — how well it does on average per trial. Pick whichever matches your use case; otherwise an unstable agent can look dependable, or a solid one can look flaky.

Read transcripts before trusting the score. A dashboard number can't tell you whether your eval measures the right thing. Sample some transcripts and check the agent's reasoning, tool calls, and final state. When a task fails, the transcript shows whether the agent truly failed or the grader rejected a perfectly reasonable answer. This habit also surfaces broken graders — naive string matching can penalize correct answers over trivial formatting — and ambiguous task specs that are impossible to complete as written. Teams have sometimes improved benchmark scores dramatically just by fixing grading bugs, with no model change at all.

And watch for suites where the agent already passes nearly everything. A 98% score is great for regression testing but tells you nothing about where to improve. Keep it as a regression guard and add harder tasks to push new capabilities.

Fitting Evals Into Your Workflow

Evals deliver the most value when they run automatically. Wrap your agent's core function and run the eval suite like unit tests on every pull request — if performance drops, block the merge before users ever see the problem.

Production monitoring still has a role. Evals give you fast, repeatable checks, but pair them with user feedback, live usage data, and periodic transcript reviews to catch edge cases and drift that a fixed test suite will miss.

Who Should Use It / Who Should Skip It

Teams building or maintaining AI agents — especially anyone who has been burned by an unexplained performance drop after a prompt, tool, or model change — will get the most from this approach. The layered-grading strategy and isolated harness design apply to agents of any complexity.

If you're only running simple, single-turn prompts, full agent eval infrastructure is probably overkill; a basic output check against expected answers will do. And if your agent is an early prototype still changing daily, a handful of manual checks may serve you better until the design stabilizes — then invest in a proper eval suite as it matures.