CONTROL BEFORE AUTONOMY
Set authority before an agent can act.
AI agent governance is the operating agreement for what an automated system may do, what it may recommend, what requires human approval, and what must never happen without direct authorization. A useful framework makes those boundaries specific enough to implement, test, monitor, and audit.
Governance should be designed with the workflow and architecture, not added after deployment. The objective is accountable operation: useful automation with named owners, visible decisions, controlled access, and recoverable failure modes.
The framework begins with the real process. If the organization cannot identify the workflow owner, the systems involved, the decision boundaries, and the consequences of a wrong action, it is too early to grant an agent meaningful autonomy.
Eight controls for a governed AI system
Authority boundaries
Define the actions the agent can take, the systems it can reach, transaction limits, prohibited actions, and the situations that require escalation.
Human approval
Place explicit review before high-consequence, irreversible, sensitive, or ambiguous actions. Record who approved the action and why.
Access controls
Give the agent only the data and tools required for its task. Use scoped credentials, separation of duties, and revocable access.
Audit logs
Capture prompts, tool calls, decisions, approvals, outputs, errors, and relevant version information without logging unnecessary sensitive data.
Escalation
Specify when the agent must stop, what context transfers to a person, how urgency is signaled, and who owns the next action.
Ownership
Name a business owner, technical owner, and review cadence. Governance fails when everyone can use a system but nobody owns its behavior.
Monitoring
Track task quality, exceptions, tool failures, unusual action patterns, cost, latency, and changes in upstream systems or model behavior.
Rollback and shutdown
Maintain a tested way to pause the agent, revoke access, reverse eligible actions, restore a safe manual path, and preserve evidence for review.
Classify actions before assigning autonomy.
Not every tool call should have the same authority. A practical governance model groups actions by consequence and reversibility, then connects each class to a clear approval rule.
| Action class | Example | Typical control |
|---|---|---|
| Read | Retrieve public service information or check published availability | May be autonomous when access is appropriate |
| Prepare | Draft a booking, recommendation, quote, or proposed update | Agent prepares; person or policy decides whether to commit |
| Write | Create or modify a business record, appointment, or customer workflow | Authenticated authority plus validation and audit evidence |
| High impact | Issue a refund, commit significant funds, accept a contract, delete important data | Explicit human approval and stronger verification |
The exact classification depends on the business. The important requirement is that the authority decision exists before the agent is given the tool.
Governance should follow the full action chain.
- Request: What did the user or system actually ask for?
- Interpretation: What action does the agent believe is appropriate?
- Authorization: Is the caller, user, or system allowed to request that action?
- Approval: Does policy require a responsible person before execution?
- Execution: Which tool or system performs the action, with what parameters?
- Evidence: What record proves what happened, what failed, and who approved it?
- Recovery: Can the action be reversed, escalated, or safely transferred to a manual process?
An AI Workflow Audit is the practical starting point when these steps are not yet mapped.
Governance becomes real through testing.
Policies need executable checks. Test whether the agent respects tool permissions, routes exceptions correctly, waits for approval, handles missing or conflicting data, and fails safely when integrations are unavailable.
Use the pre-production AI agent evaluation guide to turn controls into release evidence. Governance states the rules; evaluation demonstrates whether the specific system follows them under normal, exceptional, and adversarial conditions.
The Nova Voice case study shows a first-party example of controlled actions, human transfer, structured records, and post-call delivery in a voice AI workflow.
Questions an AI governance review should answer
Who owns the agent?
Name the business owner responsible for the outcome and the technical owner responsible for the operating system around it.
What can it actually do?
List every meaningful tool and action, not just the model’s conversational capability.
What requires approval?
Define the consequence, ambiguity, sensitivity, or transaction threshold that moves an action to a person.
How do we stop it?
Document the pause, credential-revocation, rollback, escalation, and manual-continuity procedures before launch.
AI Agent Governance FAQ
What is AI agent governance?
It is the set of operational rules, authority limits, controls, approvals, evidence requirements, and ownership responsibilities that determine how an AI agent may act inside a real business process.
Is governance only for regulated industries?
No. Any agent that can change records, communicate commitments, spend money, access sensitive information, or trigger downstream work needs clear authority and failure boundaries.
When should governance be designed?
Before production authority is granted. The AI Implementation Roadmap treats governance and architecture as part of the delivery sequence rather than a post-launch add-on.
How do you know governance is working?
By testing it. Tool permissions, approvals, escalation, exception handling, audit evidence, and shutdown paths should be demonstrated before release and monitored afterward.
Design governance around the real workflow.
Review the broader responsible AI infrastructure services, explore the wider project portfolio, or begin with an AI Readiness Audit if the use case itself still needs to be prioritized.