Treat the producer and the verifier as separate control functions, even if both are software. The actor can draft, test, and refine, but a different system must decide whether the work is acceptable. That separation preserves accountability, limits blind spots, and gives auditors a defensible decision path.
Design the Control Model Around a Split Between Creation and Approval
autonomous agent can be allowed to produce drafts, run tests, and iterate, but governance should treat acceptance as a separate decision. The key question is not whether the same software can do both jobs, it is whether one control path is being asked to authorise its own output. That is where accountability weakens and blind spots appear.
For practical governance, the producer can optimise for speed and exploration while the verifier optimises for correctness, policy, and risk tolerance. That separation is especially important when the work affects production changes, access decisions, customer outcomes, or other actions that are hard to reverse.
When teams collapse those functions into a single autonomous loop, they often lose the ability to explain why a result was accepted, what evidence supported it, and who or what had authority to approve it. A defensible control model makes the approval path independent, even if the underlying review is partially automated.
What Separation Changes in Practice
Separation is not just an architectural preference, it changes the trust model. The producing agent can still use tools, evaluate outputs, or score its own work, but those checks should be advisory unless an independent system or policy engine performs the final gate. That keeps the verifier from inheriting the producer’s assumptions.
This matters most where the agent has broad context or execution power. The more autonomy the producer has, the more valuable it becomes to constrain what counts as evidence, require explicit acceptance criteria, and route the final decision through a distinct control function. In other words, the agent may help prepare the case, but it should not be the only party deciding the case.
Teams should also distinguish between validation and self-certification. Automated tests, linting, simulations, and rule checks are useful, but they are not the same as an independent authorisation step. If the same agent writes the change and certifies that the change is safe, the control has only one real viewpoint.
Accountability, Auditability, and Governance Boundaries
Good governance creates a traceable chain from work product to approval. That means the output, the evidence used to assess it, the rule or policy applied, and the identity of the approving control must be preserved. An auditor should be able to see not only what was produced, but why it was allowed to proceed.
This is also where ownership becomes important. Product teams may own the agent’s tasking, but platform, security, or risk functions often need to own the approval boundary, logging standard, and exception process. If ownership is ambiguous, autonomous review becomes a convenience feature instead of a governable control.
For teams formalising agent access and approval patterns, NHIMG’s AI Agent Authorisation Guide is useful for separating task execution from per-action approval, and the Zero Trust for AI Agents guide reinforces the idea that each request should be verified rather than assumed safe. Where teams need observability, the AI Agent Observability, Audit and Incident Response Guide helps define the logs and attribution needed to defend the approval path.
Where Self-Review Fails and What Good Looks Like
Self-review fails most often when the agent’s incentives and the verifier’s criteria are too closely aligned. If the same system is rewarded for output quality and also given authority to certify that quality, it can optimise for passing its own checks rather than for meeting the organisation’s real standard. That is a structural weakness, not just a tuning issue.
What good looks like is a clean separation of roles, a documented acceptance policy, and review evidence that does not depend on the producer’s own narrative. Independent verification can still be automated, but it must be a different control path with separate criteria, separate logs, and a clear escalation route when confidence is low.
For mature programs, the practical test is simple: if the producing agent were compromised, confused, or overly optimistic, would the verifier still have enough independence to reject the work? If the answer is no, the control is not truly independent yet.
Risk and Threat Considerations
When an autonomous agent can both generate and certify its own work, the main risk is control collapse. The same decision-maker can introduce an error, conceal weak evidence, or normalise a bad outcome as acceptable, which reduces detectability and weakens post-incident accountability.
Failure mechanism: A single agentic loop can become a self-affirming control path, where the producer influences the criteria, the evidence, or the final acceptance decision. That creates blind spots, weakens segregation of duties, and makes it harder to detect unsafe changes before they are acted on.
Impact: Poorly governed self-validation can allow unsafe code, incorrect decisions, or overreaching actions to pass review with a false sense of assurance. In regulated or high-impact environments, that also creates a defensibility problem because the organisation may not be able to show independent approval.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 addresses the attack surface, NIST SP 800-53 Rev 5 sets the technical controls, and ISO/IEC 27001:2022 defines the regulatory obligations.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Autonomous agents can overstep authority when they judge their own output. |
| Recommendation — Separate action generation from independent approval and enforce per-action policy checks. | ||
| NIST SP 800-53 Rev 5 | AC-6 — Least Privilege | Self-validating agents need constrained authority so the producer cannot approve unrestricted outcomes. |
| AU-6 — Audit Record Review, Analysis, and Reporting | Independent governance depends on logs that show what was produced, reviewed, and approved. | |
| CA-7 — Continuous Monitoring | Agent behaviour and approval outcomes must be monitored for drift, failure, or policy bypass. | |
| Recommendation — Limit agent authority to the minimum needed and route approval through an independent control. Log producer, evidence, and approver details so acceptance decisions are defensible. Continuously monitor agent outputs and approval outcomes for anomalies or control failure. | ||
| ISO/IEC 27001:2022 | A.5.3 — Segregation of duties | The question is fundamentally about separating creation from approval authority. |
| Recommendation — Separate the creator and approver roles so no agent certifies its own work. | ||
Practitioner Guidance
What to prioritise: Establish an independent approval boundary before expanding agent autonomy. The first governance question is not how much the agent can do, but which decisions it is forbidden to make about its own output.
What to verify: Confirm that the verifier has separate policy inputs, separate evidence sources, and a distinct rejection path. If the same prompt, rule set, or scoring logic drives both production and approval, the separation is largely cosmetic.
Decision rule: If the work can cause meaningful operational, security, or customer impact, require a different control function to approve it, even when both functions are automated. Reserve self-validation only for low-impact, reversible, and tightly bounded cases.
Practitioner takeaway: The governing principle is not to eliminate autonomous production, but to prevent any agent from being the sole judge of its own acceptability.
Related resources from NHI Mgmt Group
- How should teams govern autonomous agents that can choose their own tools and timing?
- How should security teams govern autonomous systems that create their own access?
- How should engineering teams govern autonomous coding agents that can create branches, open pull requests, and change code across multiple repositories?
- How should security teams govern non-human identities at scale?