Agent assurance goes beyond model evaluation
| Question | Model evaluation | Agent assessment |
|---|---|---|
| What is examined? | Model capabilities under a defined benchmark or setup. | Configured tools, memory, retrieval, workflow and operating environment. |
| What makes results useful? | A clear dataset, method and model version. | A requirement linked to a repeatable case and evidence from the agent boundary. |
| What changes? | Model version and inference settings. | Prompts, permissions, connectors, policies and populations, even without changing the model. |
What “AI agent certification” means here
AI agent certification is market terminology, but the phrase can suggest a statutory conclusion that an ordinary product assessment cannot provide. We use assessment and assurance for the governed process described here.
AI Agent Certify does not automatically provide statutory certification, notified body assessment or a legal compliance determination. A positive result has a stated scope, method and limitations; it is not permission for every future use.
Define a reproducible boundary
Agent identity
Name the owner and exact release, including prompts, model configuration, tools and relevant connections.
Approved operation
Describe users, tasks, environments and actions allowed without further approval.
Known exclusions
Record tasks, languages, populations and integrations not evaluated. An omitted case is not a passed case.
Turn policy into observable criteria
A broad policy such as protecting confidential information needs a specific data boundary and testable condition.
Requirement
Write the obligation in terms the responsible reviewer can approve.
Control
Describe how the system should prevent, constrain or escalate the relevant behaviour.
Evaluation
Define setup, expected action and the evidence needed to judge the control.
Finding
Explain how an observation supports or challenges the criterion.
Evaluate, challenge and review
Run approved cases
Cover intended tasks, refusal, escalation and recovery behaviour.
Add red teaming
Probe misuse, conflicting instructions and unsafe tool sequences within the agreed boundary.
Inspect evidence
Distinguish a control failure from an invalid case, unavailable dependency or missing observation.
Record human authority
Document the reviewer, decision, unresolved issues and conditions on use.
What the enterprise receives
Traceable evidence
Case definitions, observations, relevant configuration and the basis for findings. Access follows agreed enterprise boundaries.
Assurance Scorecard
Results, findings, coverage and limitations. It is not a universal trust number.
Eligible Agent Passport
A separately reviewed record may be prepared where sharing is appropriate. Private evidence stays outside its public projection.
Reassess when the original basis no longer holds
Material changes to tools, instructions, permissions, use case, population or applicable requirements can invalidate the earlier assumptions. Monitoring prompts a decision about which criteria need evaluation again. Preserve the previous assessment rather than editing it to describe a different system.
Prepare the evidence for your assessment
Use the AI agent assurance evidence checklist to decide whether the observations support the proposed operating scope. For security-sensitive workflows, the security certification evidence guide connects threat coverage, critical findings and release conditions.
When seeking outside support, compare agent access certification vendor categories so identity infrastructure, behavioral testing and management-system certification are evaluated for the different work they provide.
Questions about ai agent certification and assessment
Does a positive assessment guarantee safety?
No. It describes observed results within a defined scope. Unseen scenarios, operational changes and incomplete coverage remain limitations.
Why preserve the exact version?
Evidence must identify the system that produced it. A new prompt or tool permission can change behaviour without changing the underlying model.