AI agent evals vs testing: what is the difference?

Agent evals score an AI agent's outputs on a dataset, with code checks or an LLM judge, usually with mocked tools. Agent testing runs the agent's tasks end to end against test copies of real systems and checks the result there. Evals tell you whether the answers are good; tests tell you whether the records are right.

Side by side

EvalsEnd-to-end tests
QuestionIs this model or prompt good?Did the task leave the system in the right state?
Looks atOutputs and transcriptsRecords in the connected system
ToolsUsually mockedReal integrations in a test environment, with injected failures
Good atAnswer quality, tool choice, comparing prompts and modelsWrong data, duplicate writes, hallucinated success

Which you need

  • The agent only answers or drafts text: evals plus observability cover most of the risk.
  • The agent writes into business systems: add end-to-end tests on the write tasks, run before each release. That is what a release gate does.

Benchmarks are a third thing: they help you pick a model, not ship an agent. See finance agent benchmarks vs testing.

On the blog

AI Quality System · AI agent regression testing · Shadow mode testing · All terms

FAQ

No. Evals score outputs, often with mocked tools. Tests run tasks against real integrations in a test environment and check the resulting records.

Not for writes. The judge reads the transcript and can share the agent's mistakes. Whether a record exists with the right values has to be checked in the system.

Usually yes. Evals are the faster way to compare prompts and models on answer quality. Tests cover what the agent writes.

Try it on your own agent

Describe a task in plain words. Agentiqa does it step by step and tells you if it worked. Every failure comes with step-by-step reproduction.

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Your agent writes into ERP, CRM or billing for customers? See Enterprise.