Agentic code is changing how we think about code quality | Harness Blog

As AI-generated and agent-authored code increases the volume and frequency of changes entering CI pipelines, the focus of continuous integration must expand from build speed to providing fast, actionable quality feedback. Harness argues that Test Intelligence can select tests relevant to a change by mapping tests to the code they exercise, reducing unnecessary full-suite runs, while code coverage identifies changed lines and paths that tests did not execute. Coverage alone is not presented as proof of quality, since executed code may still be insufficiently verified, but it can reveal gaps through repository, branch, and file-level trends and can be enforced through pipeline thresholds. Harness Builds combines test selection, coverage data, flaky-test identification, and Playwright-based application testing to provide multiple signals about a change’s readiness. This detailed context is particularly important for autonomous agents, which need explicit information about relevant tests, failures, uncovered paths, and remaining work to iterate effectively and support human confidence in production readiness.

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