What is AI code review? How it works, what it checks, and where agentic review fits

AI code review involves an automated process where large language models analyze code changes to provide findings for human action. The process typically begins with a trigger, such as opening a pull request, followed by the reviewer collecting context from the diff and surrounding files. Analysis methods include deterministic analysis, which relies on pattern matching, and LLM reasoning, which infers intent like a human. Findings are posted with explanations and suggested fixes, allowing developers to act upon them. The process is often enhanced by combining deterministic checks with LLM reasoning, such as in hybrid models like Endor Labs AI SAST, which orchestrate multiple specialized agents.
AI code review assesses correctness, quality and maintainability, and security. It handles correctness by catching bugs like off-by-one errors or missed updates. For quality and maintainability, it flags duplicated logic and deviations from team conventions by analyzing context from repository instructions. Security is a key focus, enabling detection of injection flaws, hardcoded secrets, and dependency risks. This review operates alongside existing tools like linters and SAST, focusing on reasoning about intent that complements rule-based analysis.
Agentic code review represents a more advanced form where AI agents perform multi-step actions using tools rather than single comments. These agents can pull broader context, call external tools for scanning, trace data across the codebase, classify findings, and propose fixes, leading to deeper, context-aware insights. While offering greater depth, agentic review introduces trade-offs in cost, latency, and the need for auditable reasoning regarding tool access and decision-making.

* AI code review runs where developers already work, appearing as PR comments, CI/CD checks, or IDE notifications.
* Agentic code review involves reviewers that take multiple steps and use tools instead of single diff comments.
* AI code review means AI acts as the reviewer, distinct from people reviewing agent-produced code.
* AI code review works by collecting context, analyzing with LLM reasoning or deterministic analysis, posting findings, and allowing a person to decide action.
* Deterministic analysis involves pattern matching, dataflow tracing, and type checks, yielding repeatable results based on written rules.
* LLM reasoning reads code like a person, inferring intent, and is probabilistic.
* Serious tools often combine deterministic analysis (for facts) and LLM reasoning (for intent).
* AI code review checks for correctness (bugs), quality/maintainability (duplication, naming), and security (injection, secrets).
* Linters enforce style rules line by line and are fast.
* Rule-based SAST matches known vulnerability patterns but struggles with complex business logic flaws.
* Agentic review involves agents pulling context beyond the diff, calling tools like scanners, tracing data across files, classifying findings, and proposing fixes.
* AI code review can fall short with noisy comments, repeated results, run-to-run variance in LLM output, context limits on large PRs, and not automatically counting toward approvals.
* Security is where AI review shows the greatest payoff because complex vulnerabilities often depend on intent across multiple files.
* Endor Labs AI Security Review runs on every pull request, utilizing detection, triage, and remediation agents.
* The approach involves specialized agents for detection, triage (classifying findings), and remediation (proposing fixes).
* In real-world testing, AI SAST found 192 vulnerabilities across eight projects in various languages.
* AURI provides security context to coding agents during writing, aiming to prevent insecure patterns from reaching the PR.

The narrative positions AI code review as an evolving layer that moves beyond syntactic enforcement (linters) and pattern matching (SAST) into reasoning about developer intent and cross-contextual flows, particularly in security. The distinction between simple LLM commenting and agentic review is crucial: the former offers quick feedback on a diff, while the latter attempts to replicate the holistic investigative capabilities of a specialized engineer by orchestrating tool usage over the entire repository context. This suggests an inherent tension between speed/simplicity and depth/accuracy in code review systems.
The shift toward agentic review implies that the most complex classes of bugs—those related to broken access control, insecure design, and business logic flaws—cannot be captured by static rules or simple pattern matching alone. The reliance on LLM reasoning to infer intent addresses this gap, but the reported shortfalls—like noisy comments and probabilistic results—highlight a dependency on strong external scaffolding (repo instructions) and robust feedback loops (triage and human sign-off).
The emphasis on security as the prime domain for AI review is not accidental; it stems from the nature of modern software risk, where flaws often emerge from complex interactions across the entire system rather than isolated errors. The focus on agentic systems suggests a future where automated review functions move toward full autonomous investigation, demanding mechanisms—like auditable reasoning and fine-grained tool control—to maintain human accountability over critical decisions. What is missing is a detailed exploration of how these multi-agent systems manage catastrophic failure modes when their probabilistic reasoning conflicts with deterministic facts, and what the long-term societal implication is for distributed cognitive authority in software development.
Bridge Questions: How should organizations structure the trust model between deterministic analysis findings and probabilistic LLM reasoning when both conflict? What mechanisms are necessary to ensure that agentic reasoning about intent remains auditable without devolving into an unmanageable log of every tool call? What shifts in human role are required as AI handles pattern recognition, allowing developers to focus entirely on architectural design and threat modeling?