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Agentic AI Governance Checklist
Most AI governance frameworks — SR 11-7, the EU AI Act, ISO 42001 — were written before agentic deployment
scaled, and treat an AI agent as a generic service account with no dedicated identity, authorization, or
accountability controls. Singapore's IMDA published the first comprehensive agentic-AI framework in January
2026; NIST launched its own AI Agent Standards Initiative the following month. This checklist maps what's
distinct about governing an agent — not a model — for teams running MCP servers or other
tool-calling AI in production. Enter your work email to unlock it; it prints cleanly to PDF from your browser.
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Unlock above to view all 20 checklist items across 4 sections.
1 · Agent Identity
Dedicated agent identity — each AI agent has its own credential (API key, service identity), never a generic or shared account, per the IMDA framework's core requirement.
Human-agent binding — every agent identity is traceable to the specific human or role that provisioned it and remains accountable for its actions.
Credential lifecycle policy — documented issuance, rotation, and revocation process for agent credentials, distinct from human user account policy.
Tier/scope inventory — a documented list of what each agent identity is authorized to do (which tools, which data, which systems), not just that it has "access."
Sub-agent and delegation tracking — if an agent can spawn or delegate to other agents, each delegated identity is separately tracked, not collapsed into the parent's identity.
2 · Authorization & Action Boundaries
Explicit tool/action allowlist — each agent's permitted actions are explicitly enumerated, not inferred from broad API scope.
Read/write separation — read-only and state-changing actions are distinguished, with state-changing actions held to a higher authorization bar — consistent with how AI agent directories and marketplaces now review submissions.
Runtime pre-execution checks — for consequential actions, a defined check (rate limit, tier restriction, business-rule validation) runs before execution, not just logging after the fact.
Multi-step action chain review — for agents that chain multiple tool calls toward a goal, the chain itself (not just each individual call) has been reviewed for unintended emergent behavior.
Cross-system tool access mapping — a current inventory of every external system an agent can reach, since agentic tool access tends to expand quietly over time as new tools are added.
Kill switch / pause capability — a tested, fast way to revoke a specific agent's access without taking down the whole system.
3 · Audit Trail & Accountability
Action-level audit log — every consequential action an agent takes is logged with which agent identity performed it, when, and under whose authorization — the specific evidentiary standard the IMDA framework names.
Reconstructable decision trail — for a given output, the tool calls and data the agent used to produce it can be reconstructed after the fact, not just the final answer.
Log retention and tamper-resistance — audit logs are retained for a defined period and cannot be silently altered by the agent itself or a compromised credential.
Override and correction logging — when a human overrides or corrects an agent action, that override is logged with the same rigor as the original action.
Incident classification — a defined severity scale for agent-related incidents (wrong tool call, unauthorized action, data exposure), consistent with how serious-incident reporting works under model-level frameworks like the EU AI Act.
4 · Cross-Framework Alignment
ISO 42001 gap coverage — documented supplementary controls for agent-specific risks (multi-agent orchestration, autonomous decision delegation), since ISO 42001 does not natively address agentic systems.
Model-level framework mapping — for each agent, which model-level governance regime also applies (SR 11-7/SR 26-2, EU AI Act, NAIC bulletin) and how agent-level controls feed into that existing documentation rather than duplicating it.
Vendor/third-party agent disclosure — if using a vendor-supplied agent or MCP server, documentation of what was independently verified about its identity, authorization, and logging versus what's taken on the vendor's representation.
Regulatory horizon tracking — a named owner responsible for tracking agentic-AI-specific guidance as it emerges from NIST, IMDA, and sector regulators, since this is the fastest-moving area of AI governance as of 2026.
Examiner-ready evidence package — agent identity, authorization scope, and audit logs assembled in a form reviewable directly, not just described in policy language.
Need this filled in, not just outlined?
We build the agent identity and audit-trail test suite, verify authorization boundaries hold under adversarial testing, and write the report your governance file can cite.
This checklist is educational, not legal advice — agentic AI governance guidance is still emerging and
varies by jurisdiction and sector. Confirm your specific obligations with qualified counsel. See our
disclaimer for more.