No, not by default. Human code review assumes a slower, more traceable workflow, while agent-driven development can create larger volumes of change and more rapid dependency shifts. Organisations should preserve review intent but adapt the controls so they validate orchestration, permissions, and interaction risk rather than only authorship.
Why the Same Review Model Does Not Fit Agent Outputs
Human code review is usually built around authored changes, clear ownership, and an expectation that one person can explain intent after the fact. Agent outputs change that assumption. The useful control question is not “who typed it?” but “what authority produced it, what dependencies did it touch, and can the resulting change be trusted within the system’s actual blast radius?”
That is why organisations should preserve review intent, but shift the control objective. For agentic workflows, the control should validate orchestration, permissions, and interaction risk, not just code authorship. A change can be technically well-formed and still be unsafe if the agent inherited too much access, chained through an untrusted tool, or created hidden dependency drift.
When the source of change is an autonomous system, the review process must account for runtime context as well as the patch itself. In practice, that means asking whether the agent was acting within a bounded task, whether its tools were approved, whether secret exposure was possible, and whether the output introduced new execution paths that human review alone would miss.
What the Control Should Validate Instead
Agent output control should be designed around three things: provenance, privilege, and propagation. Provenance means the team can trace which agent, prompt, model, tool chain, or workflow produced the change. Privilege means the agent only had the minimum access needed for the task. Propagation means the organisation understands what downstream effects the output can trigger, especially when an agent modifies build scripts, infrastructure, or dependency manifests.
That is a different control posture from standard code review. Traditional review often checks correctness, maintainability, and policy compliance. With agents, the higher-value check is whether the output is safely executable in the environment where it will land. A syntactically valid change is not enough if it was assembled from stale context, overbroad permissions, or a tool interaction that changed system state outside the reviewer’s sightline.
This is also where organisations should adapt approval thresholds. Low-risk agent outputs may be suitable for automated guardrails and sampling-based review, while high-impact outputs should require stronger human verification. The distinction should be based on change criticality and authority, not on whether the code was human-written or machine-generated. For agent-driven development, the most important question is often how much trust to grant the path, not the text of the diff.
How to Decide When Human Controls Still Apply
The practical decision rule is simple: if the agent output can alter access, secrets, production configuration, or dependency trust, treat it as a high-risk change even when the code itself looks routine. If the output is limited to low-impact refactoring, formatting, or clearly sandboxed work, lighter controls may be enough. The control strength should scale with the possible consequence of the change, not the novelty of the author.
That means organisations need different evidence for different classes of change. For routine human edits, reviewer sign-off may be enough. For agent outputs, teams should be able to show the originating task, the policy that constrained the agent, the tools it was allowed to use, and the checks that verified the output did not exceed its mandate. In other words, the control should prove bounded delegation, not just code quality.
Good practice also means separating intent review from execution review. A reviewer may approve the goal of the change, but the system should still verify that the realised output did not overstep the delegated scope. That separation becomes especially important when an agent can generate many small changes quickly, because volume can hide risk even when any single diff appears harmless.
Risk and Threat Considerations
Agent outputs can amplify risk because they may produce more changes, more dependencies, and more opportunities for subtle privilege or trust errors than a human workflow. The main exposure is not merely defective code, but change paths that inherit excessive access, reuse unsafe connections, or introduce unreviewed dependency shifts into sensitive systems.
Failure mechanism: The control fails when organisations review only the visible diff and ignore how the agent obtained authority, what tools it used, and whether the change expanded the attack surface through secrets, connectors, or automation paths.
Impact: A compromised or over-permissioned agent can push high-volume changes, spread risky dependencies, or modify production-relevant assets faster than a human reviewer can detect, increasing the chance of silent misconfiguration or abuse.
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, OWASP ASVS and CIS Controls v8 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Agent outputs can exceed delegated authority or privileged scope. |
| Recommendation — Constrain agent actions to least privilege and per-action approval. | ||
| NIST SP 800-53 Rev 5 | IA-9 — Service Identification and Authentication | Agent workflows rely on non-human services and tool identities that must be authenticated. |
| AC-6 — Least Privilege | Agent outputs should be bounded by the minimum authority needed for the task. | |
| Recommendation — Authenticate each service or agent identity before allowing privileged automation. Limit agent permissions to task-scoped access and revoke standing excess rights. | ||
| OWASP ASVS | V15 — Secure Coding and Architecture | Agent-generated changes need architectural checks beyond simple code review. |
| Recommendation — Verify that generated changes preserve secure architecture and do not expand trust boundaries. | ||
| CIS Controls v8 | CIS-5 — Account Management | Agent workflows depend on governed accounts, service credentials and approval boundaries. |
| Recommendation — Review and remove unnecessary agent-linked accounts and privileges regularly. | ||
Practitioner Guidance
What to prioritise: Start by classifying agent outputs by blast radius. Changes to auth, secrets, infrastructure, dependency management, and release automation deserve stricter controls than formatting or low-risk refactors.
What to verify: Confirm the agent’s task scope, tool access, and approval path before trusting the output. The key question is whether the agent stayed within bounded delegation throughout the workflow.
Common mistake: Do not apply the same review checklist to every change just because it entered the repository the same way. A human-authored diff and an agent-authored diff can have very different trust assumptions.
Practitioner takeaway: Keep the review objective constant, but adapt the evidence you require. For agent outputs, the real control is confidence in delegated action, not confidence in authorship alone.
Related resources from NHI Mgmt Group
- Should organisations use the same controls for human-written and AI-generated code?
- Should teams use the same controls for human, service, and agent MCP identities?
- How should organisations balance agent autonomy with human approval in code changes?
- Should organisations use the same lifecycle controls for AI agents and other non-human identities?