Agent review is the practice of evaluating what an AI agent did, why it did it, and whether the supporting checks were sound. It shifts attention from only reading code diffs to examining agent intent, execution context, and the path taken to reach a change, which is critical for trustworthy automation.
What Agent Review Is Really Checking
Agent review is not just a code-diff exercise. It asks whether the agent had the right objective, whether the context it used was appropriate, and whether the decision path makes sense given the task, the permissions it had, and the evidence it consulted. That makes review a control over both outcome quality and the trustworthiness of automation.
For teams using agentic workflows, the central question is whether the action was merely successful or also defensible. A change can look correct in the final artefact while still being produced by weak reasoning, stale context, or an unsafe shortcut that would fail under a different input. Agent review helps expose that gap.
What Reviewers Need to Examine
Effective review looks at three layers together: what the agent changed, why it chose that path, and whether the surrounding checks were actually sound. The point is to reconstruct intent and process, not only to validate the end state. That includes the prompts, tool calls, approvals, retrieved context, and any human intervention that shaped the result.
This is especially important when the agent can operate across multiple tools or systems. The same output may be produced by a careful sequence of bounded actions or by a broad, poorly constrained execution path. Review has to distinguish those cases, because the latter can hide unsafe autonomy even when the result appears useful.
Where Agent Review Adds Security Value
Agent review becomes most valuable when the agent’s permissions, tool access, or contextual inputs can influence real systems. In those cases, the review is not only about quality assurance, it is also about verifying that the agent stayed within its intended authority and did not rely on unsafe assumptions, over-broad access, or misleading context.
It also helps with accountability. When an agent makes a risky change, the review record should make it possible to understand whether the failure came from the agent’s reasoning, the policy around it, or the human supervision model. That distinction matters because the remediation is different in each case.
How Agent Review Differs From Traditional Code Review
Traditional code review focuses on the artifact. Agent review adds the execution story behind that artifact. A reviewer may still inspect the code, configuration, or content that changed, but they also need enough evidence to judge whether the path to that change was safe, justified, and repeatable.
This broader lens changes what good evidence looks like. A useful review may include the agent’s objective, intermediate decisions, approvals, tool interactions, retrieved sources, and any warning signals such as unexpected scope expansion or overconfident action. Without that trail, review can only guess at whether the automation behaved responsibly.
Risk and Threat Considerations
Agent review matters because unsafe agent behavior can be hidden behind a plausible final output. If reviewers only inspect the end result, they may miss prompt injection, tool misuse, overprivileged access, context poisoning, or a subtle drift between the task and the action actually taken.
Failure mechanism: The agent is steered by tainted instructions, weak guardrails, or excessive authority, then produces a change that appears legitimate unless the execution path is reconstructed.
Impact: Organisations can approve harmful actions, miss privilege abuse, or fail to detect that the agent reached outside its intended scope, which increases the chance of misconfiguration, data exposure, or unauthorized change.
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 and risk surface, while NIST SP 800-53 Rev 5 sets the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Agent review checks whether an agent used authority safely. |
| ASI02 — Tool Misuse | Review must reconstruct how tools were used to reach the change. | |
| ASI06 — Memory & Context Poisoning | Agent review examines whether poisoned context influenced the decision path. | |
| Recommendation — Review agent actions for privilege misuse and constrain authority to the task. Validate tool calls and block unsafe tool use paths. Inspect retrieved context and memory inputs for poisoning before approving output. | ||
| NIST SP 800-53 Rev 5 | AU-6 — Audit Record Review, Analysis, and Reporting | Agent review depends on examining records to understand what happened. |
| AC-6 — Least Privilege | Review needs to confirm the agent stayed within intended authority. | |
| Recommendation — Review audit records to reconstruct the agent’s actions and decisions. Constrain agent permissions to the minimum required for the task. | ||
Practitioner Guidance
Why practitioners should care: Agent review should be designed to answer the question, “Would I still trust this action if I could not rely on the final output alone?” That means reviewers need evidence of intent, execution path, and control effectiveness, not just a diff or summary.
What to watch for: Pay close attention when the agent makes high-impact decisions, chains multiple tools, or operates with standing access. Those are the situations where a clean-looking result can conceal weak supervision or unsafe autonomy.
Practitioner takeaway: The strongest agent review process makes the reasoning trail auditable enough that reviewers can challenge the path, not just approve the outcome.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on October 31, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org