Replace agent vibe checks with trace-based regression tests
Added to DistroCool on 2026-10-21 · Discovery date, not a release announcement
Google’s October 15 evaluation playbook shows a practical path from a handful of critical user journeys to repeatable agent tests. The open-source agent-eval toolkit can run multi-turn scenarios, ingest OpenTelemetry/OpenInference traces, apply deterministic and boolean judge checks, and report latency, token use, cost, and tool-call paths.
Why it matters
An agent can return polished prose while looping, calling the wrong tool, or triggering duplicate side effects. Freezing a small golden dataset and comparing the same traces after every prompt, model, or tool change turns those silent failures into visible regressions. The approach is framework-agnostic, although the documented automated evaluation service is on the Gemini platform.
Try this next.
Write five scenarios for one critical workflow, including one incomplete request and one tool failure. Manually review the first traces, then encode pass/fail checks such as “required tool called once,” “ID verified before action,” and a latency or token ceiling. Re-run the frozen set after one prompt change and compare the delta. DistroCool has not independently tested this example.
Open the practical workflow →Source: Google Developers Blog — October 15, 2026 ↗