Agent Pull Requests Are Everywhere. Here's How to Review Them

The pull request queue used to be straightforward. Human developers wrote code, submitted changes, and other humans reviewed it. You could trace the logic through familiar patterns, anticipate common mistakes, and even recognize a colleague's coding style from across the repository.

That world is vanishing.

Across organizations of every size, AI agents now generate substantial portions of code contributions. These agent pull requests arrive with different characteristics than human submissions: cleaner diffs, more consistent formatting, but sometimes subtle logical gaps or over-engineered solutions that pass tests yet miss the mark. The challenge for development teams isn't whether to accept this reality, but how to review effectively within it.

Understanding Agent-Generated Code Patterns

AI-generated pull requests tend to exhibit recognizable characteristics that, once understood, make review more efficient.

Structural consistency stands out immediately. Agent-authored code typically follows project conventions with remarkable fidelity, applying the correct linting rules, matching naming patterns, and organizing imports properly. This makes the mechanical aspects of review easier, but it can also mask deeper issues. The code looks right in ways that feel almost too perfect.

Comprehensive test coverage frequently accompanies agent PRs, often including edge cases a human developer might overlook. However, these tests sometimes validate the wrong thing or pass for the wrong reasons. An agent might generate tests that exercise the implementation rather than the requirement, creating a false sense of security.

Verbose documentation appears regularly, with extensive comments explaining what each section does. While this seems helpful, it sometimes states the obvious or documents implementation details that should be self-evident, rather than capturing the why behind architectural decisions.

The Review Framework

Effective review of agent pull requests requires adjusting your focus from mechanical correctness toward architectural soundness and actual problem solving.

Verify the Problem Was Solved

Start by confirming the PR actually addresses the original issue. AI agents sometimes solve a related but different problem, implementing what they interpreted from the issue description rather than what was intended. Check the linked issue, trace the requirements, and ensure the solution targets the right problem.

Examine Edge Cases

While agents often generate thorough edge case coverage, they can miss context-specific scenarios that require institutional knowledge. Look for:

• Interactions with other systems in your architecture
• Behavior under failure conditions your team has encountered before
• Compliance with business rules that aren't explicitly documented
• Performance implications for your specific scale and usage patterns

Challenge the Implementation

Agent-generated code frequently works but isn't optimal. Ask whether:

• The solution adds unnecessary complexity
• Simpler alternatives were overlooked
• The implementation couples components that should remain independent
• This change creates technical debt that will compound over time

Inspect Tests Critically

Test quality matters more with agent PRs because the implementation confidence feels higher than it actually is. Evaluate whether tests:

• Verify behavior, not implementation
• Would catch regressions if requirements changed
• Include meaningful assertions beyond "output equals expected"
• Cover the scenarios your users actually encounter

Establishing Team Workflows

Beyond individual review techniques, teams benefit from explicit policies around agent-generated contributions.

Documentation requirements help capture institutional knowledge that agents cannot know. Require PRs to include context about why certain decisions were made, what alternatives were considered, and how this change fits into broader architectural direction.

Sign-off protocols clarify responsibilities. Some teams require human authorship for certain critical paths, while others allow agent-generated code with enhanced review scrutiny. The specific policy matters less than having an explicit one.

Iterative agent collaboration often produces better results than single-pass generation. Encourage developers to work with agents iteratively, refining requirements and providing feedback, rather than accepting first-draft implementations.

The Trade-off Reality

Reviewing agent pull requests requires more skepticism but can ultimately be faster. The consistency agents bring reduces mechanical review burden, freeing time for higher-value architectural scrutiny. However, teams must resist the temptation to reduce review rigor simply because the diff looks clean.

The efficiency gains materialize when reviewers shift their focus from syntax and style which agents handle well toward the strategic questions that still require human judgment: Is this the right solution? Does it serve our users? Does it move our architecture in the right direction?

As agent capabilities continue advancing, these review patterns will evolve. The fundamentals, though, remain constant: verify the problem was solved correctly, examine what the tests don't cover, and always apply human judgment to architectural decisions. The author changed from human to AI. The reviewer's job remains fundamentally the same.

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Andrea is a Senior Developer Advocate at GitHub. Find her online @acolombiadev.