Agent Access Review Checklist for AI Agents
An AI agent access review checks whether each agent still has the right owner, purpose, scopes, tools, endpoints, payment authority, public records, logs, and retirement state. The review should prove that every permission is intentional, current, auditable, and tied to a named agent identity.
For HeadlessDomains.com, the review has both private and public evidence. IAM shows internal permissions. A .agent record, agent.json manifest, SKILL.md file, and Headless Profile Directory page show what outside agents, partners, marketplaces, and auditors can inspect.
Access Review Checklist
| Review area | Question | Evidence | Action if unclear |
|---|---|---|---|
| Owner | Who answers for this agent? | Registry owner, sponsor, support path | Pause new access until ownership is assigned |
| Purpose | Does the agent still perform the approved task? | Manifest purpose, launch doc, usage logs | Retire or reapprove the agent |
| Scopes | Are permissions limited to current work? | IAM grants, OAuth scopes, service account roles | Remove unused or broad grants |
| Tools | Which MCP tools, APIs, and apps can it call? | MCP metadata, gateway logs, API keys | Disable unknown or stale connections |
| Public identity | Can others inspect the official record? | .agent record, agent.json, profile page | Publish or update the public identity |
| Lifecycle | Is the agent active, paused, replaced, or retired? | Registry state and directory status | Start offboarding if the state is stale |
How Often to Review Agents
Review high-risk production agents monthly, payment-capable agents after payment policy changes, external-facing agents after every endpoint change, and lower-risk internal helpers at least quarterly. Any agent connected to customer data, administrative tools, MCP servers, or partner workflows deserves a documented review cadence.
Google Cloud's MCP authentication guidance separates user-delegated access from service accounts and agent identities. That distinction should appear in every access review because an agent using a human's credentials creates different audit and revocation questions than an agent with its own identity.
Review Workflow
- Export the current agent inventory from IAM, MCP gateways, app integrations, and public directories.
- Match every entry to a named owner, sponsor, business purpose, and lifecycle state.
- Compare actual permissions against the approved manifest and task list.
- Check whether the agent still uses every endpoint, tool, scope, and payment authority.
- Verify that public-facing records match internal state.
- Remove broad, unused, inherited, or stale access.
- Flag agents that require offboarding, ownership changes, or incident review.
- Record reviewer, date, evidence, findings, and next review window.
Example Access Review Record
{"agent":"inventory-reorder.agent","owner":"commerce-ops","reviewer":"security-team","review_date":"2026-05-20","scopes_reviewed":["catalog:read","order:create"],"removed":["customer_export"],"public_record":"https://inventory-reorder.agent/.well-known/agent.json","next_review":"2026-06-20"}Where HeadlessDomains.com Fits
HeadlessDomains.com turns access review findings into public inspection evidence. When a review confirms owner, endpoint, capability, and status, teams can reflect that state in the .agent record and profile page. When a review fails, teams can mark the identity as paused, restricted, replaced, or retired.
Pair this review with AI agent offboarding so failed reviews lead to clear state changes rather than quiet exceptions.
Related Reading
- AI Agent Identity Security
- The Agent Registry Checklist
- Shadow Agents Are the New Shadow IT
- AI Agent Incident Response
FAQ
What is an AI agent access review?
An AI agent access review is a periodic check that confirms each agent has a current owner, approved task, limited permissions, inspected tools, correct public records, and a lifecycle state.
Who should own the review?
Security should set the review standard, but the agent owner should attest to purpose, scopes, tools, and lifecycle state. High-risk findings should route to IAM, platform, legal, or incident teams.
What evidence should reviewers collect?
Collect registry entries, IAM grants, OAuth scopes, service accounts, MCP metadata, gateway logs, endpoint lists, public manifests, directory profiles, and recent action logs.
What should happen after a failed review?
Remove unused access, pause risky endpoints, update public records, start offboarding, or trigger incident response if logs show suspicious activity.