Build an AI Code Review Loop
Catch what agents get wrong before a human reads the PR.
/ quick answer
Insert an automated review layer that checks the classes of mistakes agents actually make. Catch what agents get wrong before a human reads the PR.
- 01Run deterministic gates first: typecheck, lint, tests, bundle size. No AI needed.
- 02Add an AI reviewer with a specific rubric: error handling, security, dead code, scope creep.
- 03Require the reviewer to cite file and line for every finding — no vague advice.
- 04Auto-block on security and scope-creep findings; treat style notes as comments.
- 05Loop the findings back to the coding agent for one fix attempt before human review.
- 06Track which finding types recur and promote them into project rules.
What does the Build an AI Code Review Loop workflow do?
Insert an automated review layer that checks the classes of mistakes agents actually make.
What problem does Build an AI Code Review Loop solve?
Agent-generated PRs arrive faster than humans can review, so review quality collapses.
How many steps does Build an AI Code Review Loop take?
6 steps. It starts with run deterministic gates first: typecheck, lint, tests, bundle size. no ai needed. and ends with track which finding types recur and promote them into project rules..
Which tools does Build an AI Code Review Loop need?
It uses coding-agent-stack, ai-testing-stack — each linked below with its own node.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →AI Software Engineering
AI software engineering is the practice of building software where agents write most of the code and humans own architecture, review and verification.
- →Coding Agent
A coding agent reads a repository, plans a change, edits files, runs tests and iterates until the task passes — instead of just suggesting snippets.
- →AI Testing
AI testing covers two things: using AI to generate and maintain tests, and testing AI systems whose output is non-deterministic.
- →Evals
Automated tests that grade LLM outputs against expected behavior.
Related workflows
Turn this into a repeatable process.
- →Build a Test Suite for a Non-Deterministic AI Feature
Grade probabilistic output without brittle snapshot tests.
- →Run a Coding Agent on a Real Codebase Safely
Give an agent write access without letting it wreck main.
- →Build an MCP Server for Your Own App
Expose your product's capabilities to every AI client with one server.
- →Build a Research Automation Pipeline
Question in, sourced structured brief out — on a schedule.
Related tool stacks
The tools that run it in production.
- →Coding Agent Stack
Run coding agents with executable feedback and reviewable diffs.
- →AI Testing Stack
Test deterministic code and probabilistic AI output in one pipeline.
Related prompts
Reusable prompts for this job.
- →AI Code Review Rubric Prompt
A reviewer prompt that finds real defects instead of style opinions.
- →Coding Agent Task Spec Prompt
Writes the task brief that keeps a coding agent inside scope.
- →AI Test Generation Prompt
Generates behavioural tests and edge cases, not tautologies.
- →Build An Alert Workflow Prompt
Designs a complete alerting pipeline — events, sources, thresholds, deduplication and delivery — from a plain description.
Related use cases
How people apply it, and what came out.
- →Two-Founder Startup Ships Like a Team of Six
Coding agents plus a hard verification gate replaced three planned hires.
Comparisons & alternatives
Pick between the options.
- →Coding Agent vs Autocomplete Copilot
Copilots accelerate typing; coding agents complete tasks. Different unit of work, different review burden.