How do you know when an AI agent is ready for production?
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One question I've been thinking about is when an AI agent is actually "ready" for production.
Traditional software often has clear testing processes, but AI agents introduce a different level of uncertainty. They rely on reasoning, external tools, changing context, and non-deterministic outputs, making it difficult to define a clear launch criterion.
How does your team make that decision?
Is there a checklist, a target success rate, a set of benchmark tasks, or simply enough confidence after internal testing?
Have you ever deployed an agent that looked ready in development but behaved very differently with real users?
I'd love to hear how different teams decide when an AI agent is ready to move from testing into production.
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The checklist people reach for is a success rate, and it is the wrong shape. A 95% agent is not 95% good, it is an agent that fails one time in twenty, and whether that ships depends entirely on what the failure costs and whether anyone notices. Two agents at the same score are completely different products if one writes a draft and the other sends it. What I would gate on instead: how far does a wrong action reach before a human sees it, and can the agent tell you it is unsure rather than failing confidently. An agent that says "I do not know" at 80% is more shippable than one that is silently wrong at 95%, because the first failure mode has a handler and the second does not. Reliability is a property of the system around the agent, not of the agent. What is your current failure cost per wrong action?