An AI-native pipeline keeps normal engineering checks: tests, types, security scans, builds, and migrations. It adds behavioral evaluation for prompts, retrieval, models, and agents.
Run a stable regression dataset on relevant changes. Compare quality, latency, cost, and tool behavior against the baseline. Block critical regressions, review ambiguous cases, and record version identity in production.
Release gradually when risk warrants it, then evaluate live traces. Production failures should become new offline cases. CI/CD becomes a loop between known expectations and real behavior.