AI Agent Governance

AI Agent Governance: Control What Agents Can Do

Q: What is AI agent governance?

AI agent governance is the system of identities, permissions, policies, data controls, and audit evidence that determines what an agent may do and proves what it did. The controls need to operate during the run, not only describe it afterward.

Q: How is agent governance different from model governance?

Model governance focuses on models, data, risk assessments, and lifecycle oversight. Agent governance also controls runtime authority: which systems and tools an agent can use, what data it can access, which actions it can take, and when it must stop or escalate.

Q: Do governed agents need human approval for every action?

No. A governed agent can complete pre-approved, lower-risk actions autonomously within explicit boundaries. Exceptions and higher-risk actions can be routed to human review without allowing the agent to define its own authority.

Q: What should an agent audit trail contain?

A useful run record includes the agent and version, the tool proposed and its arguments, the gateway decision, any approval asked for and answered, redactions recorded by kind, and the action executed with its outcome.

Q: Does agent governance make an organisation compliant?

No platform can make that determination by itself. Agent governance can provide traceability and operational evidence that supports EU AI Act and ISO/IEC 42001 review workflows, alongside the organisation's wider legal, risk, and management-system responsibilities.

Q: Is existing identity and access management enough for AI agents?

IAM remains essential for workload identity, credentials and access. Agent governance also binds that access to an approved job, evaluates the resolved business action against the agent and version, verified caller, tool, arguments, remit, purpose, agent intent, authored rules, task, and reason, then routes exceptions and preserves the complete delegation and outcome record.

Q: Do regulated AI agents need human approval for every action?

No. Routine, lower-impact and reversible actions can be pre-approved within explicit limits. Consequential, ambiguous, exceptional or difficult-to-reverse actions should follow a defined human-review path with expiry and state revalidation.

Q: What happens if the agent governance service is unavailable?

Failure behaviour should be defined by action impact. A consequential action may fail closed or route to review, while a low-risk workflow may switch to read-only mode. Retries, timeouts, duplicate-action protection and evidence of the degraded state must be designed before production.

Q: How should an enterprise start governing AI agents?

Start with one bounded workflow that has a named owner, known systems, a measurable outcome, a small number of consequential actions and a clear human fallback. Test permitted, prohibited, ambiguous and failure paths before expanding authority.

Define each agent's identity and authority. Enforce policy, permissions, and data controls as it works. Keep evidence of every run.

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Definition

What Is an AI Agent Governance Platform?

An AI agent governance platform is the operating layer that determines who an agent is, what job it owns, which resources it may reach, and which actions it may take. It applies those decisions during execution and records the evidence needed to review the outcome.

This is different from a policy register that documents intended controls, and different from an observability tool that reports activity after it happens. A governed agent should act only inside authority the organisation approved in advance.

Traditional AI governance still matters. It covers use cases, models, risk, policy, accountability, and lifecycle oversight. Agent governance adds the operational controls required when an AI workload can choose tools, retrieve changing data, update business records, and continue through several steps. The unit that must be governed is the complete run, not only the model request.

During the Run

How Agent Governance Works

A governed run begins by resolving the requesting user, the agent identity, its owner, and the job it is approved to perform. The platform loads the systems, records, tools, actions, thresholds, data rules, and fallback paths that apply to that job. Those controls form the agent's operating boundary.

When the agent requests a consequential action, the platform evaluates the resolved request against current authority and policy. It can allow the action, narrow its scope, redact protected data, block it, or route it to human review. The action should not reach the target system until the relevant decision has been made.

The resulting evidence should connect the agent and version, the tool proposed and its arguments, the gateway decision, any approval asked for and answered, redactions recorded by kind, and the action executed with its outcome. That gives engineering, security, risk, operations, and the business owner one defensible record of what happened.

The Operating Model

Four Controls That Belong Around Every Agent Run

Identity and ownership

Give every agent a defined job, a distinct identity, and an accountable owner before it reaches a business system.

Pre-approved authority

Define the systems, data, tools, and actions the agent may use instead of granting open-ended access.

Controls during execution

Apply permissions, organisational policy, and sensitive-data protection while the agent works.

Evidence after every run

Keep one accountable record of attempts, policy results, blocks, fallbacks, actions, and outcomes.

Control Library

Explore the Agent Governance Control Stack

Use these practical guides to design and evaluate each part of the governed run.

Agent identity and ownership

Bind each workload to a distinct identity, an approved job, and an accountable owner.

Permissions and access control

Limit systems, records, tools, actions, thresholds, and delegated authority.

Runtime policy enforcement

Evaluate consequential actions before the target system changes state.

Audit trails and run evidence

Connect identity, policy results, actions, fallbacks, and outcomes in one record.

Credential boundaries and network egress

Keep the agent from holding a credential and limit which outbound destinations a run can reach.

PII redaction for agents

Transform protected fields before they cross a restricted boundary.

Human approval and fallback

Route high-impact actions and exceptions to an accountable review path.

MCP governance

Approve servers and tools, validate arguments, control credentials, and preserve the action chain.

2026 buyer guide

How to Evaluate AI Agent Governance Platforms

Buyer Checklist

What to Look For in an Agent Governance Platform

Evaluate whether the controls change what the agent can do, not only how the run is reported.

Authority model

Can you define what each agent may access and which actions it may take?

Runtime enforcement

Can the platform block or route an action before it reaches a consequential system?

Sensitive-data controls

Can configured PII and data protections operate in the same path as the agent run?

Credential boundary

Can the agent be prevented from holding credentials or reaching a system directly?

Accountable evidence

Can one record explain who acted, what was attempted, which policies applied, and what happened?

Existing-system fit

Can agents work across approved enterprise systems without replacing the systems of record?

Compliance Evidence

Operational Evidence, Without the Compliance Claim

The EU AI Act includes obligations such as risk management, activity logging, documentation, and human oversight for certain AI systems. ISO/IEC 42001 establishes requirements for an organisation-wide AI management system.

Difinity can contribute agent-level evidence about identity, policy results, data handling, actions, and outcomes. That evidence can support review workflows, but it does not by itself make an organisation compliant or certified.

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EU AI Act Evidence for Governed Agents

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ISO/IEC 42001 Evidence for Governed Agents

Compare Approaches

Building, Observing, and Governing Are Different Jobs

Agent builders create the workflow. Observability tools explain technical behaviour. Governance platforms control authority during execution and retain evidence for accountable review.

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See How the Platform Categories Differ

Questions

AI Agent Governance Questions

What is AI agent governance?

How is agent governance different from model governance?

Do governed agents need human approval for every action?

What should an agent audit trail contain?

Does agent governance make an organisation compliant?

Is existing identity and access management enough for AI agents?

Do regulated AI agents need human approval for every action?

What happens if the agent governance service is unavailable?

How should an enterprise start governing AI agents?

Start With One Agent and One Bounded Job

Bring the systems, data, and actions involved. See how pre-approved authority, runtime control, and accountable evidence fit around the run.

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