How to Build an Agent-Native CI Pipeline

Agent-native CI is presented as a bounded approach in which an automated agent examines artifacts from failed test runs, classifies the cause, and prepares an evidence-based diagnosis or proposed fix before a human investigates, while leaving merge decisions to human reviewers. Using GitHub Actions’ `workflow_run` event, a triage workflow can react to a failed test workflow, retrieve its artifacts, and operate within GitHub’s three-level workflow chaining limit, though its potentially elevated access to secrets and write tokens requires careful permissions controls. Effective triage depends on structured evidence such as screenshots, network logs, console output, and failure records, which can distinguish application defects from stale tests, infrastructure failures, and configuration problems; the text cites TestMu AI’s HyperExecute and Kane CLI as products that provide such evidence collection and AI root-cause analysis. Recommended safeguards include preventing agents from editing the tests they diagnose through path allowlists, requiring successful reruns to validate fixes, using narrowly scoped tokens, and delivering all proposed changes through pull requests. The approach can accelerate diagnosis of failures that tests already detect, but it cannot address untested flows, unclear requirements, or automation designs that exceed GitHub’s workflow chaining constraints.

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