Evaluating AI agents: moving past the vibe check

AI agent evaluation should move beyond informal “vibe checks” because plausible final responses can conceal retry loops, incorrect tool use, excessive token costs, latency, and harmful duplicate side effects. A progressive evaluation strategy begins by defining critical user journeys with measurable requirements, creating realistic synthetic scenarios through user simulators, and manually reviewing early execution traces to identify actual failure patterns. These findings can then be translated into layered tests that combine unit tests, deterministic assertions, boolean LLM-as-a-judge checks, and integration testing while measuring operational factors such as latency, token consumption, API cost, and tool-call counts. The approach recommends preserving a stable golden dataset, favoring pass/fail criteria over inconsistent numeric judge scores, and standardizing traces with OpenTelemetry and OpenInference so evaluations work across frameworks such as LangGraph, Google ADK, and custom runtimes. Tools including the open-source agent-eval toolkit can capture traces, run checks through Gemini’s Agent Evaluation service, produce diagnostic reports, and eventually support CI/CD merge gates, shared dashboards, and continuous incorporation of production failures into the test suite.

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