What is agentic AI code review?
Agentic AI code review is how a team checks code that an agent wrote before it ships. The human stops reading every line and reviews the intent, the evals and the scope of the change, while review agents scan the diff first. One thing stays fixed: a named person still approves the merge.
New here? I publish one agentic AI governance post every weekday. Subscribe to the blog and it lands in your inbox the moment it goes live.
You cannot read every line an agent writes. You can review what it was asked to do.
Why can’t humans review agent code line by line anymore?
The volume broke it. Anthropic says code output per engineer grew 200 percent in a year, and before it added automated review, only 16 percent of its pull requests got substantive comments. That is a team skimming, not reviewing.
The code is also messier. CodeRabbit looked at 470 open-source pull requests and found that AI co-authored ones carried about 1.7 times more issues than human-only ones. Logic and correctness problems were 75 percent more common.
So there is more code, with more defects, and the same reviewers. Something has to give. In most teams I talk to, it is the review. Approvals turn into a habit, and a habit is not a control.
What does the human review instead of the diff?
In agentic AI code review, the human checks three things before reading any code:
- First, did the change do what the intent.md asked, and nothing more?
- Second, did the eval suite pass at the agreed threshold?
- Third, did the agent touch files, tools or permissions outside its scope?
That third check catches the failure I worry about most. A clean, well-tested change that nobody asked for is still a defect. I reject it even when the code is fine.
| What changes | Traditional code review | Agentic AI code review |
|---|---|---|
| What the human reads | Every line of the diff | Intent, eval results, scope of change |
| Who reads the diff first | A senior developer | Review agents, then a human on flagged areas |
| Pass signal | Two approvals | Evals pass, gates green, one named approver |
| Main risk | A missed bug | An approved change outside the intent |
| Evidence kept | PR comments | Trace, review findings, approver’s name |
Should an agent review another agent’s code?
Yes, as the first pass, but never as the approver. Anthropic’s own review tool raised the share of pull requests with substantive comments from 16 percent to 54 percent. Engineers marked fewer than 1 percent of its findings as incorrect. Still, the company is blunt that the tool will not approve a pull request, because that stays a human call.
That split is right. I covered why in my post on subagents checking each other, and in the rule that the agent that wrote it cannot approve it. Review agents find bugs. Humans own the decision.
The cost is real, though. Anthropic puts a review at 15 to 25 dollars. For a pricing rule in a loan system, that is cheap. For a typo fix, it is not, so I tier it.
More on the Agentic AI SDLC
- The Contract: What a services deal must say when agents do the work.
- Who Signs What: The ten roles and 24 activities in my agentic AI RACI matrix.
- The Human Gate: Where a person must stop the agent, and where a gate is just friction.
- The Assumptions: Ten habits from the old lifecycle that agents quietly break.
Where do I still read every line?
I read every line where a mistake moves money, grants access or touches personal data. That means payment logic, credentials, permission changes, regulated calculations and anything that writes customer records. In my work in regulated loan origination, credit and pricing logic always gets a human line review.
Everything else goes through agentic AI code review by intent, evals and scope. Honestly, I would rather read 300 lines that matter than skim 3,000 that don’t. The risk tier decides which is which, and it is written down before the agent starts.
How does the pull request change?
The pull request has to carry the evidence, not just the diff. Mine links the intent, lists the files touched against the files allowed and shows the eval results. Then it shows which review-agent findings were fixed and who approved.
A reviewer can check that in five minutes. If something is missing, the change goes back. That rule does more for agentic AI code review than any tool I have tried, because it makes skipping a step visible.
Who signs the merge?
A named human signs every merge, and the record shows who. Instructions in, results out was IT. Intent in, outcomes out is agentic AI. Agentic AI code review keeps that promise by moving the human from reading lines to owning outcomes.
I wrote the long version of this operating model in Intent In, Outcomes Out and the autonomy side in Earned Autonomy.
One question to leave with. How many lines did your team approve last week without reading them? If nobody knows, start there.
Go deeper
- Agentic AI Architect: control design for enterprise agents.
- Agentic AI Case Studies: deployment evidence, one teardown at a time.
- Agentic AI P&L: cost, payback, and risk in a CFO’s voice.
- Agentic AI SDLC: the lifecycle that builds the agents.
- Subscribe to the Blog: I publish a new post every weekday on one agentic AI governance question. Enter your email and each post arrives in your inbox the moment it goes live.
© Dr. Harish Kotadia, Ph.D., All Rights Reserved, 2026
Dr. Harish Kotadia, Ph.D., is an Enterprise AI Architect with 20+ years of IT consulting experience serving Fortune 100 clients, specializing in agentic AI governance and architecture for regulated enterprises.
Disclaimer: This blog post is based on publicly available academic publications, vendor documentation, open standards, and news items from reputed media sources linked above. This post is intended for educational purposes, to help the enterprise agentic AI community build a shared vocabulary from public, authoritative sources.
Views and opinions expressed here are my own and do not represent those of any employer or client, past or present. The analysis presented is my independent interpretation of the published sources linked above and does not constitute legal, financial, or consulting advice of any kind.