Behavioral unit tests isolate one expected agent action, such as choosing the correct return tool in an ecommerce workflow. Exact assertions work for deterministic details, while an LLM judge can score subjective qualities against a clear rubric when wording or reasoning may vary.
Integration tests evaluate the complete agent rather than one step. A research agent can be judged on the final report, source quality, citation coverage, factual accuracy, and other weighted criteria, giving teams a practical way to catch regressions across a multi-step workflow.
Online evaluations apply similar measures to real production traces. Teams can monitor turn count, response length, tone, escalation behavior, latency, and task completion, then sample or alert on changes instead of waiting for users to report that agent quality has drifted.
Benchmarks package representative tasks, ground truth, and scoring into repeatable comparisons. Łucek uses software engineering and financial-audit examples to show how benchmarks reveal where one agent configuration performs better than another without reducing every result to a single vague score.
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