How do I test something that gives different answers each time?

Define success as a property rather than an exact string, such as "calls the search tool" or "includes the order number," and run each case several times to measure how often the property holds.

What's the single most useful debugging tool for agents?

A complete, readable trace of each run. Almost every other technique depends on being able to see exactly what the agent did, in order, including its tool calls and their results.

Should I mock external tools during testing?

Yes, for repeatable tests. Mocking removes variability from outside systems so you can tell whether a change in behavior came from your edit or from the external service responding differently.

How to Test and Debug AI Agents

Learn how to test and debug AI agents using traces, evaluation sets, replay, and isolation techniques that turn unpredictable behavior into fixable bugs.

Testing an AI agent is harder than testing ordinary software because the same input can produce different outputs, and failures happen deep inside multi-step reasoning. The way through is to make agent behavior observable, build repeatable test cases, and isolate problems one layer at a time. This guide covers a practical workflow for both.

Capture Full Traces of Every Run

You cannot debug what you cannot see. Before anything else, instrument the agent to record a complete trace of each run: the prompt, every model response, every tool call with its arguments and results, and the final output. A trace turns a vague "the agent did something weird" into a concrete sequence you can read step by step.

Good traces show the agent's reasoning alongside its actions, so you can tell whether a failure came from a bad decision or a bad tool result. Store traces in a way you can search and replay later. Most debugging sessions start by opening the trace of a failed run and finding the exact step where things went off the rails.

Build an Evaluation Set

Ad hoc testing misses regressions, so assemble a set of representative tasks with known good outcomes. Include common cases, tricky edge cases, and the specific failures you have already seen. For each, define what success means: an exact answer, a required tool call, a format, or a looser quality judgment.

Run the whole set whenever you change the prompt, tools, or model, and compare results against the previous run. Because outputs vary, run each case a few times to see how consistent the agent is, not just whether it passed once. The evaluation set becomes your safety net against changes that fix one thing and quietly break another.

Isolate the Layer That Failed

Agent failures usually trace back to one of a few layers: the prompt, a tool, the model's reasoning, or the surrounding code. Debug by isolating each. If a tool returned bad data, test the tool directly with the same arguments. If the agent reasoned poorly over good data, the problem is in the prompt or model, not the tool.

Reproduce the failing step on its own whenever you can. Feed the agent the exact state that led to the bad decision and watch what it does. Narrowing the problem to a single layer keeps you from rewriting the whole prompt when the real issue was a malformed tool response.

Use Replay and Controlled Variation

Because agents are nondeterministic, fixing a bug means confirming it stays fixed across many runs, not one. Replay the failing scenario repeatedly after a change to see whether the fix holds. When you want stricter reproducibility, pin randomness and mock external tools so the only thing varying is what you are testing.

Controlled variation also helps you find brittleness. Slightly reword inputs, reorder information, or change tool results and see whether the agent still behaves. An agent that passes one phrasing but fails a paraphrase has a fragile prompt that needs firmer instructions.

Watch for Common Failure Patterns

Certain bugs show up again and again. Agents loop when a tool keeps failing and they keep retrying it; they hallucinate when a lookup returns nothing and they fill the gap; they ignore instructions buried too deep in a long prompt. Learning to recognize these patterns in traces speeds up diagnosis enormously.

Once you spot a pattern, fix it at the source. Add a stop condition for loops, add fallback handling for empty tool results, and move critical rules higher in the prompt. Keep a running list of the failure modes you have seen so new agents inherit the fixes instead of repeating the mistakes.