A polished demonstration can show that a concept works with selected inputs. It does not establish how the system behaves with varied users, unexpected data, real permissions, or failures. Before widening access, gather evidence that matches the task and its impact.
Define the use
Describe the user, the problem, the expected outcome, and the actions the system may take. Identify what is outside its scope. If the use case or owner is unclear, the next step is clarification rather than broader access.
Test representative conditions
Check ordinary tasks, edge cases, ambiguous requests, missing information, and conditions that should cause the system to stop or ask for help. Evaluate the result against criteria that matter to the user. Record known limitations and how changes to models, tools, or data could affect behavior.
Plan for real operation
Confirm that permissions match the intended scope, a responsible person can review consequential actions, and the team knows how to pause, recover, and respond to incidents. Consider privacy, security, accessibility, support, and operating costs in the actual environment.
Expand when evidence supports it
There is no universal checklist or numeric threshold that makes every agent production-ready. The necessary evidence depends on capability, impact, and obligations. Treat evaluation as ongoing work: monitor relevant outcomes, review meaningful failures, and reassess when the system or its context changes.