AI agents are moving quickly from experimentation into real business workflows. Teams are using them to research information, assist customers, analyze documents, write code, call tools, reach enterprise systems, and increasingly to take actions on their own.
If your organization is building or adopting agents, a practical question follows: how do you know when one is ready for production?
Traditional software testing answers part of it. Model evaluation answers another part. But an agent is more than a model. Its behavior also depends on the instructions it is given, the context assembled around it at run time, what it remembers, which tools and data it can reach, the permissions it holds, and the environment it operates inside.
That makes readiness a broader question than accuracy, and a broader question than a passing test suite.
This guide sets out a practical, end-to-end way to answer it. The approach comes out of building evaluation infrastructure for AI systems and working with teams who had to decide, on a particular Tuesday, whether a particular agent could go live. It organizes the problem into four layers, tracks readiness along four measurable dimensions, and turns the result into a release decision somebody can defend six months later.
The material assumes you understand roughly what generative AI and AI agents are. It does not assume any prior exposure to ProofAgent, to readiness indexes, to adversarial multi-turn evaluation, to context assessment, or to policy as code. Each of those is introduced from first principles as it becomes useful.
It is written for the people who share the decision: engineering and platform leaders, chief AI and technology officers, security and risk teams, compliance professionals, product managers, and the AI engineers who build and evaluate the agents themselves.
Keep one real agent in mind as you read — ideally the one you are least comfortable putting into production.
At various points you will be asked what that agent is allowed to do, what it is built from, what evidence you hold about its behavior, and who would own the decision to release it. The questions are more useful when they have a specific answer.
Each chapter closes with a short section called Apply This Chapter. The boxes are meant to be ticked against a real agent rather than read past, whether you work through them alone, with your team, or as a group exercise.