agentclash-ci-release-gate

Automate AI agent evaluation in CI/CD pipelines by comparing candidate builds against established performance baselines to enforce regression promotion policies before production deployment.

Prevent agent regressions in GitHub Actions by integrating automated release gates that fail builds based on custom regression policies and evidence thresholds compared to historical run data.

Baseline comparison for AI agent release gating is the process of validating candidate agent manifests against historical run data to detect performance degradation or regressions before deployment.

Yes, you can validate candidate agent manifests against a production baseline automatically by applying manifest-based validation and artifact collection to generate a release verdict within your CI/CD workflow.

Yes, the release gate approach works with challenge packs and regression suites to automatically validate candidate agents, collecting evidence to determine if builds meet the required promotion thresholds.

Automated regression promotion policy enforcement is limited to environments like GitHub Actions and requires established performance baselines and historical run data to accurately detect regressions and fail builds.