Agentic code review

Agentic code review is the use of an AI agent or model-based judge to review, triage, comment on, or repair code changes, where the reviewing system needs explicit boundaries because it can often also modify the code, tests, or comments it is judging.

How it works

An agentic code review system reads a change, the surrounding context, and the project's conventions, then produces review output: comments, a triage of risk, or, in more capable forms, a suggested change or follow-up fix. It runs as part of the pipeline rather than as a one-time prompt, often wired into the pull-request flow so new changes, and optionally new pushes, get an automated pass over the files it supports. Because the reviewer is a model, its judgment is probabilistic, so it is treated as one layer that catches a class of issues rather than a gate that certifies correctness. The hard design question is the boundary: a system that can write code and review it, or review and then repair it, is no longer an independent check. So the power to accept and ship a change stays outside the reviewing agent, with a human or a human-owned gate, and vendors document this directly, framing AI review as something to supplement with human review rather than replace it. The agent surfaces issues and proposes fixes; the standard a change must clear and the decision to ship it stay outside it, in a review bar and merge gate that humans own.

Why it matters

Agentic code review matters because AI-generated code can make review the bottleneck: more change arrives faster, but the context-rich scrutiny it needs does not speed up with it, and in some teams the pressure shows up instead as CI noise, triage load, or abandoned changes. An AI reviewer can absorb some of that load, catching some mechanical and pattern-level issues at a volume a human cannot sustain, though it should be read as coverage rather than completeness. The risk is treating its approval as certification rather than triage, because a model reviewer carries its own blind spots and can be wrong with confidence. When the same agent authors a change and supplies the sole approval, the independent check collapses; a separate reviewer model improves the separation but can still share correlated blind spots, so it is not a substitute for external evidence and a human-owned bar. Human ownership of what ships only helps when the reviewer is named, has the context, and applies an explicit bar, because otherwise it is accountability paperwork rather than independence. The useful framing is added coverage with an owned boundary: let the agent expand what gets looked at, and keep the power over what ships outside it.

In practice

An AI reviewer is wired into the pull-request flow so that every change, including changes other agents wrote, gets an automatic first pass that flags risky diffs, comments on convention breaks, and sometimes proposes a fix. A developer still owns the merge, reading the agent's comments as input rather than as a verdict, and the branch's required checks, the explicit bar, must pass regardless of what the reviewer said. When the reviewing agent also has the ability to edit code, its proposed fixes are routed back through the same review and gate rather than applied straight to the protected branch. The agent widens what gets reviewed; the decision to accept stays outside it.

Practical considerations

Keep the merge authority and the reviewing agent separate, because a system that can write, review, and apply its own changes has collapsed the independence that made the review worth running. State the review bar explicitly, the checks that must pass and what an approval certifies, so the agent's comments are input to that bar rather than a substitute for it. Expect both false positives and missed issues, and tune the agent to catch a useful class of problems rather than treating it as complete. Match the boundary to the agent's capability: comment-only review is lowest risk, a suggested patch needs re-review, a direct branch edit needs the same checks a human edit faces, and approval or merge authority should sit outside the generating or reviewing agent. Route any fix the agent proposes back through the same gate that guards human changes, rather than letting an agent-applied edit bypass the checks a person's edit would face. Watch for reviewer fatigue of a new kind, where a flood of low-value AI comments trains developers to ignore the channel, which wastes the benefit it was meant to add.

Defined by Ready Solutions AI