Agentic Regression Testing: What to Delegate, What to Verify

Agentic regression testing uses AI agents to decide which tests to run after code changes, prioritize them, and sometimes repair broken test steps, aiming to reduce the cost and duration of full regression suites without losing meaningful coverage. Its central risk is that skipped tests can hide defects silently, so the recommended approach is to delegate impact-based test selection and locator repair while retaining human approval for assertion changes, test retirement, and coverage decisions. The text proposes an autonomy ladder that begins with advisory recommendations, progresses to selective execution backed by nightly full-suite runs, and permits self-repair only with review, while cautioning that autonomous assertion rewriting can make real defects appear resolved. Safety should be measured through recall on skipped tests, comparing agent-selected subsets with scheduled full runs and tracking whether omitted tests uniquely detected faulty changes. Research cited suggests targeted impact analysis can substantially reduce regressions and testing infrastructure costs, whereas vague instructions for agents to “run tests” may worsen outcomes. Effective adoption depends on stable test histories, dependency mapping that includes configurations and shared fixtures, always-run critical paths such as authentication and payments, and ongoing audits of escaped faults and coverage changes rather than pass rates alone.

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