Continuous AI Agent Testing: From CI Gate to Production Loop

Continuous AI agent testing is presented as a lifecycle-based approach that replaces one-time benchmarks with repeated evaluations before code merges, in CI pipelines, before releases, and after production incidents. The approach addresses behavior changes caused by model updates, probabilistic outputs, stale knowledge sources, and shifts in user queries, with run-over-run comparisons used to distinguish regressions from flaky scenarios. A blocking CI gate can prevent failing agent changes from being merged by using pass/fail exit codes and standard reports, while scheduled evaluations and production feedback help detect drift and convert real failures into future regression tests. Testing must also reflect the operating environment, particularly for web-acting agents that require real browser sessions rather than mocked pages. The proposed ownership model assigns AI engineers responsibility for scenarios and fixes, QA for gates and testing cadence, product for acceptable-risk decisions, and compliance for using reports as audit evidence. The text recommends starting with one high-volume production agent, establishing a clear behavioral baseline, validating results manually, then gradually enabling blocking and scheduled testing.

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