Evaluating an agent is harder than evaluating a single model output, since an agent's final result depends on a whole sequence of decisions — which tools it called, in what order, how it recovered from errors — and "did it get the right answer" alone misses a lot of what actually determines whether an agent is reliable enough to ship.
Agent evaluation means systematically measuring an agent's performance on representative tasks — task success rate, tool-calling accuracy, efficiency (steps taken, cost, latency), and failure mode analysis — using a defined eval set of tasks with known expected outcomes, run regularly to catch regressions as the agent's prompt, tools, or underlying model change.
Why Agent Evaluation Matters (and When Manual Spot-Checking Suffices)
Systematic evaluation matters once an agent is headed toward production use, especially for consequential tasks — without a defined eval set, you have no reliable way to know whether a prompt change, a new tool, or a model upgrade improved or regressed actual behavior, and you're left relying on anecdotal impressions from whatever cases happen to come up.
Manual spot-checking suffices during early, exploratory development, when you're iterating quickly on fundamentally different approaches and a full eval suite would slow down that exploration — formal evaluation earns its investment once you're refining a largely-settled approach and need to detect smaller regressions reliably.
Getting Started with Agent Evaluation
A basic eval harness running an agent against a defined task set:
type EvalCase = {
task: string;
expectedOutcome: (result: AgentResult) => boolean;
};
const evalSet: EvalCase[] = [
{
task: "Find the current status of order #4521",
expectedOutcome: (result) => result.finalAnswer.includes("shipped"),
},
{
task: "Cancel order #4521 if it hasn't shipped yet",
expectedOutcome: (result) => result.toolCalls.some((c) => c.name === "check_status") &&
(!result.finalAnswer.includes("shipped") ? result.toolCalls.some((c) => c.name === "cancel_order") : true),
},
];
async function runEvals(agent: Agent, cases: EvalCase[]) {
const results = await Promise.all(cases.map((c) => agent.run(c.task)));
const scored = results.map((result, i) => ({
task: cases[i].task,
passed: cases[i].expectedOutcome(result),
steps: result.toolCalls.length,
cost: result.totalCost,
}));
return {
passRate: scored.filter((s) => s.passed).length / scored.length,
avgSteps: scored.reduce((sum, s) => sum + s.steps, 0) / scored.length,
results: scored,
};
}
Core Agent Evaluation Concepts Every Developer Should Know
Task success rate alone misses efficiency and correctness-of-process issues — an agent that reaches the right final answer via an unnecessarily long, expensive, or fragile sequence of steps has a real problem even if the pass/fail metric looks fine. Tracking steps taken, cost, and latency alongside success rate surfaces these issues a binary pass/fail metric hides.
Eval cases should cover both the golden path and realistic failure/edge cases — a tool returning an error, ambiguous input, a task that should be declined or escalated rather than attempted — since production traffic reliably includes these, and an eval set covering only clean, ideal-case tasks won't catch how the agent handles the messier reality.
Verifying the process, not just the final answer, matters for agents where how something was accomplished is part of correctness — the cancel-order example above checks that the agent actually checked status before canceling, not just that it produced a plausible-sounding final response; a wrong process that happens to produce a right-looking answer is a real risk worth catching.
Running evals regularly (on prompt changes, tool changes, model version updates) turns evaluation into a regression-catching tool, not a one-time exercise — the value compounds specifically because agent behavior can shift in subtle ways from changes that seem unrelated, and only a consistently-run eval set catches that reliably.
Common Mistakes Evaluating Agents and How to Fix Them
Mistake 1: measuring only final-answer correctness, missing efficiency problems or process errors that don't show up in a simple pass/fail check. Fix: track steps taken, cost, and process correctness (were the right tools called, in a sensible order) alongside outcome success.
Mistake 2: an eval set covering only clean, golden-path tasks, missing how the agent handles errors, ambiguity, or edge cases it will actually encounter in production. Fix: deliberately include realistic failure and edge-case scenarios in the eval set, not just ideal-case tasks.
Mistake 3: running evals only once, at initial development, rather than as an ongoing regression check. Fix: run the eval suite on every meaningful change to prompts, tools, or the underlying model, treating it as a continuous quality gate rather than a one-time validation.
When Should You Build a Formal Eval Suite Instead of Relying on Manual Testing?
Build a formal eval suite once an agent is headed toward production, especially for consequential tasks where undetected regressions have real cost — the eval set becomes your primary defense against silent behavior changes from prompt, tool, or model updates. Manual testing suffices during early exploratory development where approaches are still changing significantly and a formal eval suite would be premature overhead.
Agent Evaluation in Production
Track process correctness and efficiency alongside final-answer success, since a passing binary metric can hide real problems in how an agent reached its answer. Cover realistic failure and edge cases in your eval set, not just clean golden-path tasks, and run the full suite on every meaningful change rather than treating evaluation as a one-time exercise.
If your agent doesn't yet have a defined eval set and is headed toward production use, building one — even a modest set of ten to twenty representative tasks with clear success criteria — is worth prioritizing before further prompt or tool changes.