Code Review in AI-Assisted Teams

Code review has always been about catching problems humans miss. With AI-generated code, the problem set changes. Humans now need to catch architectural violations and domain misunderstandings that AI might make. Style and syntax errors? Linters handle those. The review focus shifts to correctness and context.

Review velocity matters more now because agents generate code constantly. A review process that handled ten commits a day needs to handle fifty or a hundred. You can't afford slow reviews. But you also can't afford rubber-stamping AI code. The balance is critical.

Why This Matters

AI code is fundamentally different from human code. It's often more verbose. It might not follow your architectural patterns perfectly. It might misunderstand your domain. It might over-engineer a simple solution. A human might write the same function in 10 lines; an agent might write 30. Both are correct, but one is better.

Reviewers need to understand what the AI was thinking. If an agent generated code based on your specifications, the reasoning trace matters. You can see what problem it was solving, what constraints it considered, what alternatives it rejected.

Teams that don't adapt their review process to AI-generated code hit two problems: bottlenecks (they can't review fast enough) and misses (they approve bad code because they're rushing).

How Review Changes with AI

Volume increases. An agent can generate a complete feature. Instead of reviewing 300 lines of carefully crafted code from a human, you're reviewing 1000 lines of generated code. Reviewing more code in the same time means reviewing faster per line, which means less thoroughness.

The solution isn't to review less carefully. It's to review differently. Don't check every line. Check the architecture. Check the domain logic. Let linters and tests catch the rest.

Context is paramount. A human who wrote code can explain it. They know why they chose one approach over another. An agent has reasoning traces. Read those first. They tell you what the agent was solving for.

Reasoning trace:
"User requested a function to calculate order total including tax.
Constraints: must handle multiple tax rates, must round correctly, must support coupons.
Approach: created a composable discount system so new discount types can be added.
Alternative rejected: hardcoding tax calculation (too inflexible)."

This trace tells you what to review for. You're not guessing the intent.

Architectural violations jump out. When code is consistent, violations are obvious. Code that doesn't follow your patterns stands out. An agent that generated code using a pattern you forbade is immediately visible in review.

Edge cases might be missed. Agents generate code based on examples and specifications. If your spec doesn't mention edge cases, the agent might not handle them. You need to ask: what happens when this is empty? What happens when that's null? What happens on the network boundary?

What to Look For in AI-Generated Code

Does it follow your architectural patterns? If your codebase structures error handling a certain way, does the generated code follow that pattern? If all your services have a dependency injection setup (a SOLID principle), does the generated code respect it?

// If your pattern is this:
const service = new UserService(database, logger);

// But AI generates this:
const service = new UserService();
service.database = database;
service.logger = logger;

// That's a violation. Not catastrophic, but inconsistent.

Is it correctly solving the domain problem? This is the hardest question and requires domain knowledge. If you asked for "calculate shipping cost," is the agent calculating it correctly? Does it handle the edge cases you know about?

An AI might generate a shipping calculation that's mathematically correct but wrong for your business. Maybe you have special rules for large orders, or international shipping, or perishable items. The agent won't know these unless they're specified.

Is it over-engineered? Agents sometimes generate more complex solutions than needed. They might create abstraction layers for single-use functions. They might anticipate future use cases you'll never have. It's not wrong, but it's harder to maintain.

// AI-generated version might be:
class DiscountCalculator {
  constructor(discountStrategies) { ... }
  applyDiscounts(items) { ... }
}

// But for your current use case, this might suffice:
function calculateDiscount(items) { ... }

Does it handle errors and edge cases? Does it validate inputs? Does it have reasonable error messages? Does it handle missing data gracefully?

Is it testable? Can you easily write tests for this code? If it has tight coupling to external systems, it's hard to test. If it's modular, tests are straightforward.

Practical Review Checklist

Create a review checklist specific to AI-generated code. Here's a template:

• Architecture
  • [ ] Follows established patterns (dependency injection, error handling, logging)
  • [ ] Respects architectural boundaries
  • [ ] Doesn't violate any documented constraints

• Domain Logic
  • [ ] Correctly solves the stated problem
  • [ ] Handles documented edge cases
  • [ ] Is appropriately scoped (not over-engineered)

• Testing
  • [ ] Includes tests for happy path
  • [ ] Includes tests for error cases
  • [ ] Tests have reasonable coverage

• Maintainability
  • [ ] Code is readable and follows conventions
  • [ ] Complex logic has comments explaining intent
  • [ ] No duplicate code that should be shared

• Performance & Security
  • [ ] No obvious performance issues
  • [ ] No obvious security issues (SQL injection, auth bypass)
  • [ ] No hardcoded secrets or credentials

• Documentation
  • [ ] API changes are documented
  • [ ] Complex logic is explained
  • [ ] Deprecated patterns are noted if changed

Balancing Thoroughness and Velocity

You can't review every line of AI-generated code at the same depth as human code. You need a tiered approach.

First tier: automated checks. Linters, type checkers, tests. These catch syntax, style, and basic correctness. Let them run first.

Second tier: architectural review. Does it fit your system? Does it follow your patterns? This is quick if patterns are clear. You're spot-checking, not line-by-line reading.

Third tier: domain review. Is it solving the right problem? This requires domain knowledge and is harder to automate. This is where careful review happens.

Fourth tier: spot checks. For critical code (payment processing, security, core algorithms), read it carefully. For typical CRUD operations, spot-check a few functions.

The key is being explicit about what level of review each change gets. Security-critical code gets tier 4. Feature code gets tier 2-3. Routine updates get tier 1.

Reasoning Traces and Review Experience

When agents provide reasoning traces, reviews become more efficient. Instead of trying to understand why the agent made a choice, you see the reasoning. You can agree or disagree with the reasoning, then check if the code implements it correctly.

Agent reasoning:
"Considered three approaches:
1. Recursive - elegant but stack overflow risk on large datasets
2. Iterative with stack - safe but complex
3. In-memory map - O(n) space, O(n) time, clear logic
Chose #3 because data size is bounded and clarity is important."

Reviewer: This reasoning is sound. Let me verify the implementation matches it.

With this context, review is faster because you're not reverse-engineering intent.

Anti-Patterns to Avoid

Rubber-stamping. Reading the PR title, seeing tests pass, and approving without reading code. This defeats review entirely. Even with AI code, you need some human judgment.

Excessive scrutiny. Treating every generated line like you would code from a junior engineer. Some things are fine being generated. A simple CRUD operation? Approve it if tests pass. Core algorithm? Review carefully.

Ignoring reasoning traces. If the agent provided reasoning, read it first. It shapes how you review. It shows where misunderstandings might exist.

Approving before tests run. Don't approve code until automated tests pass. Tests catch bugs that code review misses.

Blocking on style. If linters are running, don't block on code style. That's what linters are for. Focus on substance.

Review Velocity and Quality

Good review practices for AI code are:

• Linters run automatically and code can't be approved if they fail
• Unit tests must pass
• Type checking (if you use it) must pass
• Architectural review is async and reasonably quick (next day max)
• Domain review for critical code is careful but not bottlenecked

A PR should move from open to approved or requesting changes within a day. Anything longer and context is lost.

FAQ

Primary Sources

• Robert Martin's handbook on writing clean, reviewable code and best practices. Clean Code
• Nicole Forsgren's research on code review effectiveness and team performance metrics. Accelerate
• Google's engineering practices documentation on code review standards and feedback. Google Eng Practices
• The Pragmatic Programmer's approach to collaborative development and code quality. Pragmatic Programmer
• Steve McConnell's guide to code construction and peer review effectiveness. Code Complete
• John Ousterhout's principles for designing reviewable and maintainable code. Philosophy of Design
• Google SRE practices for code quality and operational excellence. SRE Workbook