Reviewing Agent-Generated Pull Requests Before They Reach Production

AI coding agents can now create complete pull requests, including source-code changes, tests, configuration updates, and documentation.

This can reduce development time, but it introduces a new responsibility for engineering teams: reviewing code that was written or modified by an autonomous agent.

A pull request generated by an AI agent should not be treated as safe simply because the code compiles or the automated tests pass.

The agent may misunderstand an existing requirement, introduce an unnecessary dependency, overlook an edge case, or modify code outside the requested scope. It may also generate tests that verify its own implementation without covering the actual business requirement.

For example, an agent asked to improve payment validation might correctly reject negative amounts but accidentally remove support for a previously accepted payment method.

The new code may pass its tests while still breaking an existing workflow.

A reliable review process must therefore examine the purpose of the change, the actual code diff, test coverage, security implications, and deployment risks.

This article explains how to review agent-generated pull requests systematically, automate useful checks, and establish approval rules before AI-generated code reaches production.

Why AI-Generated Pull Requests Need a Different Review Approach

Traditional code reviews focus on changes made by developers.

Reviewers examine the implementation, compare it with the requirements, and check whether the code follows the team's engineering standards.

AI-generated pull requests introduce additional questions:

• Did the agent correctly understand the original requirement?
• Did it modify only the intended files?
• Are the changes consistent with the existing architecture?
• Were the tests written to validate the requirement?
• Did the agent introduce new dependencies?
• Are there security or performance implications?
• Can the team reproduce the agent's validation results?

These questions do not mean AI-generated code is necessarily less reliable than human-written code.

They mean that the review process must account for how the changes were produced and what evidence supports them.

The same engineering standards should apply to both human-written and AI-generated code.

Step 1: Verify the Pull Request Scope

Start by checking whether the pull request addresses the requested task.

An AI agent may modify more files than expected because it discovers related code while exploring the repository.

Some additional changes may be necessary, but unrelated modifications should be investigated.

For example, a request to fix a validation issue should not automatically result in:

• A complete controller redesign.
• Unrelated dependency upgrades.
• Changes to application-wide configuration.
• Renaming unrelated classes.
• Formatting changes across the entire repository.

Use Git to inspect the changed files:

git diff --name-status main...HEAD

This command lists files changed between the base branch and the current branch.

To inspect the size of the changes:

git diff --stat main...HEAD

Review the output before examining individual code sections.

Compare the Changes With the Original Task

Suppose the original request was:

Add validation to reject negative order amounts
before creating an order.

The pull request modifies:

Services/OrderService.cs
Models/OrderRequest.cs
Tests/OrderServiceTests.cs
Controllers/UserController.cs
appsettings.json

The first three files appear directly related to the requirement.

The controller and configuration changes need additional investigation.

They may be necessary because the validation depends on request handling or configuration. However, the agent should provide a clear reason for each additional change.

A useful review question is:

"Can every modified file be connected to the original requirement?"

If the answer is unclear, request clarification or remove unnecessary changes.

Step 2: Review the Pull Request Description

The pull request description should explain the problem, implementation, and validation results.

An AI-generated pull request should include enough information for another developer to review it without reconstructing the entire agent conversation.

A useful description contains:

For example:

## Problem

Order creation accepted negative order amounts.

## Implementation

Added validation in OrderService before
the order persistence operation.

## Files Changed

- OrderService.cs
- OrderRequest.cs
- OrderServiceTests.cs

## Tests

- Negative amount validation
- Zero amount validation
- Valid order creation

## Validation

- Build: Passed
- Unit tests: Passed

## Review Notes

Existing integrations should be checked to confirm
that zero-value orders are handled correctly.

The description should reflect actual changes and executed commands.

An agent must not report a successful test run simply because it generated a test file.

Step 3: Examine the Code Diff

The pull request description provides an overview, but the diff is the primary evidence of what changed.

Use:

git diff main...HEAD

This displays the changes introduced by the branch compared with the base branch.

Review each important modification.

Focus on:

• New conditions and validation rules.
• Modified method signatures.
• Changes to exception handling.
• Database queries and transactions.
• Authentication and authorization logic.
• Configuration changes.
• Dependency modifications.
• Deleted or renamed functionality.

Check for Unintended Behavior Changes

Consider this original implementation:

public bool IsOrderValid(decimal amount)
{
    return amount > 0;
}

An AI agent changes it to:

public bool IsOrderValid(decimal amount)
{
    return amount >= 0;
}

The code change is small, but the behavior has changed.

The original method rejected zero-value orders. The updated method accepts them.

The reviewer must determine whether that change matches the business requirement.

A successful build cannot answer this question.

Review the intended business behavior and identify whether the new condition is correct.

Step 4: Verify Test Quality

AI agents often generate tests alongside implementation changes.

This is useful, but test existence is not proof of correctness.

A test can pass while failing to validate the actual requirement.

Consider the following implementation:

public bool IsOrderValid(decimal amount)
{
    return amount > 0;
}

A corresponding unit test might be:

[Fact]
public void IsOrderValid_WithPositiveAmount_ReturnsTrue()
{
    var service = new OrderService();

    var result = service.IsOrderValid(100);

    Assert.True(result);
}

This test verifies a valid positive amount.

However, it does not verify that negative and zero amounts are rejected.

A stronger test suite should cover multiple input conditions:

[Theory]
[InlineData(100, true)]
[InlineData(0, false)]
[InlineData(-10, false)]
public void IsOrderValid_ReturnsExpectedResult(
    decimal amount,
    bool expected)
{
    var service = new OrderService();

    var result = service.IsOrderValid(amount);

    Assert.Equal(expected, result);
}

Review whether the tests cover:

• Normal input.
• Boundary values.
• Invalid input.
• Null or missing values where applicable.
• Exception behavior.
• Existing application workflows.
• Regression scenarios.

Check Whether Tests Actually Exercise the Changed Code

A test may pass without reaching the modified method.

For example, a test might mock the service method instead of executing its implementation.

Review test setup, mocks, assertions, and execution paths.

The purpose is to confirm that the test validates the behavior introduced by the pull request.

Step 5: Run Independent Build and Test Validation

Do not rely exclusively on the agent's reported results.

Run the required validation commands in a controlled CI environment or local development environment.

For a .NET application:

dotnet restore
dotnet build --no-restore
dotnet test --no-build

For a Java application using Maven:

mvn clean verify