How AI Agents Should Be Certified for Production: Evidence Over Builder Confidence

A software development team building AI agents for transactional use cases — spanning chat, voice, and SMS — has outlined a framework for certifying when an agent is ready for production deployment. The framework argues that certification should be driven by documented evidence rather than internal team sign-offs, since builders certifying their own work face an inherent conflict of interest. Four core conditions must be met: the agent's authority must be strictly bounded, all input data must have verified provenance, the model's outputs must be independently evaluated, and every approval decision must be fully replayable and auditable. The team emphasizes that some elements of the described system are already live in production, while others remain extrapolated conclusions drawn from completed work. The approach positions a structured evidence file — not a launch meeting or leaderboard score — as the definitive admission criterion for a production AI agent.