Agent Assurance: Test Before Release, Assure After

Agent Assurance is presented as a continuous quality discipline that combines rigorous pre-release testing with ongoing production verification for AI agents, whose runtime-generated actions cannot be fully predicted or enumerated in advance. Before deployment, teams should test agents’ permitted tools, permissions, expected outcomes, known risks, and ambiguous requirements; after deployment, they should monitor for boundary violations, outcome degradation, behavioral drift, and failures in the monitoring systems themselves. This approach is especially important for agent-to-agent workflows, where automated systems may interact without real-time human oversight. Production findings should be converted into new pre-release tests, creating a feedback loop in which both agents and their verifiers are scrutinized for errors, blind spots, and false alarms. As observability and evaluation tools increasingly converge in the market, the framework emphasizes that humans must retain authority over agent permissions, definitions of failure, interpretation of evidence, and final release decisions.

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