How to Set a Clear Production-Readiness Bar for AI Agents Before You Ship

Teams deploying AI agents often struggle to define what 'production-ready' actually means, leading to subjective debates and agents shipped on gut feeling rather than measurable criteria. Unlike traditional services, AI agents are non-deterministic, have open-ended failure modes, and can produce confident wrong answers that are harder to detect than outright crashes. Experts recommend defining four concrete thresholds before building: an acceptable task-success rate, a classification of tolerable versus dangerous failures, a cost and latency ceiling, and a containment plan that limits the damage when the agent errs. Setting these benchmarks after the fact is considered unreliable, as teams tend to rationalize existing performance into acceptable ranges once a working system is in hand. The goal is to replace subjective readiness debates with a written, pre-agreed standard that turns 'is it ready?' into a measurable answer rather than an opinion.