Agentic Testing Life Cycle: The QE Loop for AI Agents

Agentic testing life cycle is a quality engineering approach for autonomous AI agents that replaces fixed specifications and conventional assertions with evidence-based evaluation, reflecting the finding that many agent codebases lack usable manifests or declared contracts. TestMu AI’s Agent Assurance implements a six-phase loop—discovery, scenario generation, profiling, execution, judging, and reporting—that derives tests from code and declared context, runs agents in real environments, and evaluates individual criteria using observed tool calls, files, artifacts, and other evidence rather than self-reported transcripts. It generates functional, non-functional, and adversarial scenarios, while separating deterministic discovery from model-assisted code exploration to avoid inventing unknown agent capabilities. Results distinguish passes, failures, and “Unable to Verify” outcomes; unverifiable criteria are excluded from pass-rate calculations and reported as an assurance gap, intended to measure how much agent behavior lacks sufficient evidence. The framework argues that observability features such as tool-call audit logs reduce this gap and make agents more verifiable. For CI, it differentiates product defects from harness or infrastructure failures through exit codes, recommends testing against staging because agent actions may have real effects, and advises teams to measure the assurance gap before using it as a build-gating threshold.

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