Deterministic AI Code Review: How It Works Alongside LLM Review

Deterministic AI code review checks code with rules, syntax-tree analysis, and standard tools instead of a model, so the same code always gets the same findings and score. That makes it usable as a merge gate. LLM review finds different problems; this guide shows how to split the work between them.

Claude Code, Cursor, Codex, Gemini, and Copilot produce more code than teams used to review. The costly part is code that passes tests but slows the reviewer with silent error handling, generic names, unused leftovers, or type-system escape hatches.

Deterministic checks catch what you can specify

If line 42 swallows an exception today, a deterministic gate still flags it tomorrow, whoever runs it. It is good at:

• Compilation, types, formatting, lint, tests, and coverage thresholds.
• Known vulnerability, secret, dependency, and policy rules.
• Stable code-quality patterns and complexity limits.
• Generated-code residue such as empty catches, unsafe casts, debug output, unresolved stubs, and dead code.
• Organization rules that need a clear pass, fail, suppression, and audit trail.

An AI code quality gate adds what a standard linter misses: patterns agents repeat at scale, such as comments that restate code, thin wrappers, TODO stubs, dead exports, and scaffolding nobody deleted. Background: what AI slop is and AI generated code quality.

The limit is the rule itself. A deterministic checker will not notice that a payment refund breaks a product policy unless someone encoded that policy in a test, rule, or data contract.

LLM and agentic review catch what needs context

LLM review helps with "does this match intent?" and is weak at "did this empty catch land again?" An agentic reviewer can also read related files, history, issues, and docs, so it can ask whether a migration misses a reader or a fallback hides an outage. It is good at:

• Cross-file logic and missing call-site updates.
• Mismatch between the issue, implementation, and repository conventions.
• Edge cases that require understanding a business workflow.
• Test ideas for behavior not represented in the current suite.
• Alternative explanations and adversarial scenarios a human can verify.

Its findings can be plausible but wrong, depend on which context it picked, or change between runs. Treat them as hypotheses that need checking, like generated code.

Benchmarks show agentic review misses a lot

CR-Bench finds that pushing a reviewer to find every hidden issue also raises spurious findings. SWE-PRBench reports that eight models detected 15% to 31% of human-flagged issues when given only the diff. In the c-CRAB benchmark, the evaluated agents together solved around 40% of tasks.

The methods differ, so these figures should not be combined into one accuracy number, but together they show that a quiet agentic reviewer is no evidence a change is safe.

Give each layer one job

Mixing the layers goes wrong in predictable ways. A model used as a linter wastes tokens re-finding dead code. Static rules treated as architecture review give false confidence. When both comment on everything, developers get duplicate findings and start dismissing the queue. Blocking on probabilistic suggestions makes releases unpredictable.

Split review into four jobs instead:

1. Mechanical hygiene. Unused imports, trivial comments, dead code, debug leftovers, formatting, duplicate imports. Auto-fixed where possible.
2. Deterministic risk. Swallowed exceptions, unsafe type assertions, hallucinated imports, hardcoded secrets, dependency vulnerabilities, risky shell or SQL construction. Named rules and a quality gate, because the answer should not change between runs.
3. Agent repair and agentic hypotheses. The scanner hands file paths, rule IDs, and fix priorities back to the coding agent. An agentic reviewer gets the diff, acceptance criteria, and selected context, and looks for correctness and security issues, not style.
4. Human judgment. Product intent, architecture, edge cases, naming that carries domain meaning, and whether the PR should exist at all.

Run the deterministic gate first, so the LLM and the human never see residue a machine can name. If an LLM keeps finding the same repeatable issue, make it a test or rule. If judging a pattern needs product intent, improve the review prompt or agent instructions instead.

LLM tools such as CodeRabbit work in the PR conversation; aislop works as the gate. See the compare pages and aislop vs CodeRabbit.

Decide what can block

A review workflow for AI-generated code

Run the deterministic gate before the PR, not only on it:

1. The agent writes or edits code.
2. A local hook or manual scan runs immediately.
3. Auto-fix clears mechanical findings.
4. Remaining findings are handed back to the agent with exact context.
5. The developer reviews the result and opens the PR.
6. CI re-runs the same pinned deterministic gate in a clean environment and publishes artifacts.
7. An optional LLM or agentic reviewer looks for contextual bugs, not style.
8. A human verifies agentic findings, inspects risk boundaries, confirms intent, and accepts or rejects the change.
9. The dashboard tracks score drift, noisy rules, and where teams need help.

The human reviewer then checks tests first, then intent, security boundaries, and architecture. Checklist: how to review AI written code.

Set up the gate

Get a baseline with npx aislop scan, or scan only what changed on a branch with npx aislop@latest scan --changes. Add aislop ci to pull requests with failBelow at or just under the current score, and raise it as you clear high-confidence findings. Tutorial: add a quality gate in 2 minutes.

The free aislop CLI covers the local loop (scan, score, auto-fix, hand findings to your agent) without an LLM, and does not hunt for contextual bugs. The hosted scanaislop platform adds PR gates, shared standards, and dashboards.

Measure review drag, not comment volume

Track whether reviewers save time:

• How many mechanical findings were auto-fixed before PR review?
• How often did the PR score regress below the team threshold?
• Which rules fire most often after agent-written changes?
• How many findings reach human review after the handoff loop runs?
• For an LLM reviewer: unique issues that led to a fix, time spent dismissing comments, and defects that escaped to production.

Use them to tune rules, agent instructions, and thresholds. More: AI code review metrics.

To start, run npx aislop scan on a repo and decide which findings should become your team's gate.