Validated AI agent, defined | Lama Consultants

A validated AI agent is an AI agent that has documented evidence it does what it is specified to do, repeatably, within a defined risk boundary. Validation means the agent was specified, tested against those requirements, and the results recorded, following GAMP 5 risk-based practice for computerised systems.

Validation answers a question that comes before any single output: does this agent do what we said it would, every time, inside the limits we set? You specify the requirements, scope the data and tools the agent can reach, test it against that specification, and keep the records. With a large language model you validate the agent and its boundaries, not the model's internals. The GAMP 5 risk-based approach is how we keep that effort proportionate to the risk of the task.

Validation and the audit trail are two halves of the same job. Validation gives you the up-front evidence the agent meets its spec. The audit trail, access scoping, and human sign-off then carry that confidence into every output it produces in live use. We run the work inside the client's own Claude Enterprise tenancy, which keeps validation records and data access under their control and supports the 21 CFR Part 11 expectations regulated teams have to meet.

Common questions

How is a validated AI agent different from an audit-ready one?

Validation is the evidence that the agent meets its specification, gathered before it goes live. Audit-readiness is what lets you defend each output afterward. A well-built agent needs both: it is validated against its requirements, then it runs with an audit trail and human sign-off.

Can you validate an agent that uses a large language model?

Yes, by validating the agent and its boundaries rather than the model internals. You specify what the agent must do, scope the data and tools it can reach, test it against those requirements, and record the results under a GAMP 5 risk-based approach.