Introduction

Pull request reviews are an important part of software development, but they can become difficult when a change touches many files or when reviewers need to understand unfamiliar code.

A reviewer may need to check whether the implementation follows the project's architecture, whether tests cover the change, whether an API introduces a security issue, and whether the code creates a regression somewhere else.

AI can help with this work by reviewing code in the context of the repository instead of looking at individual snippets.

Visual Studio's Git and AI-assisted development capabilities are moving in this direction through the Git Agent experience. The goal is not to replace human code review. Instead, the agent can help developers inspect changes, understand the pull request, identify potential problems, and prepare useful review feedback before or alongside a human review.

This article explains how an AI-assisted Git review workflow can be used in Visual Studio, what developers should ask the agent to examine, and how to keep AI-assisted pull-request reviews practical and safe.

What Is Visual Studio Git Agent?

A Git agent is an AI-assisted development workflow that can work with a repository and its Git changes to perform tasks that require more than generating a code snippet.

For pull-request review, the important difference is context.

A traditional AI prompt might contain:

Review this method for bugs.

The agent-based workflow can instead work with the repository and the change being reviewed.

Conceptually, the workflow looks like this:

Pull Request
     |
     v
Changed Files
     |
     v
Repository Context
     |
     v
Git Agent
     |
     +----> Analyze implementation
     |
     +----> Inspect related code
     |
     +----> Check tests
     |
     +----> Identify risks
     |
     v
Review Findings
     |
     v
Developer Review

This makes the review more useful because a change rarely makes sense when isolated from the rest of the application.

Why Use AI for Pull-Request Reviews?

A pull request may contain a small code change that affects a much larger part of the system.

For example, changing:

public async Task<Order> CreateOrder(OrderRequest request)
{
    ...
}

may affect:

A reviewer who only reads the changed method may miss an important dependency.

An AI agent can help investigate these relationships and provide another perspective.

It can be particularly useful for:

The result should be treated as an additional review signal, not an automatic approval mechanism.

How an AI Pull-Request Review Works

A practical review can be divided into several stages.

Step 1: Understand the Pull Request

Start by asking the agent to summarize the change.

For example:

Review the current pull request.

First explain:
1. What problem the pull request is solving.
2. Which files were changed.
3. Which application components are affected.
4. What the most important behavioral changes are.

This gives the reviewer a map before looking at individual findings.

Step 2: Review the Changed Code

Next, ask the agent to inspect the implementation.

A useful prompt is:

Review the changed code for correctness.

Look for:
- Logic errors
- Null handling problems
- Incorrect assumptions
- Exception handling issues
- Breaking behavior
- Unnecessary changes

Only report findings that have a concrete reason behind them.

The final instruction is important.

Without it, an AI review can become a list of speculative concerns rather than actionable findings.

Step 3: Inspect Related Code

A pull request should not always be reviewed only from the diff.

Ask the agent to inspect the code surrounding the change:

For each important change, inspect the related callers and dependencies.

Identify whether the change can affect:
- Existing API consumers
- Database operations
- Background services
- Authentication or authorization
- Shared libraries
- Configuration

This is where repository-level context becomes particularly useful.

Reviewing Tests

A pull request that changes behavior should normally have appropriate tests.

AI can help identify gaps.

For example:

Review the tests changed in this pull request.

Determine:
1. What behavior is currently tested.
2. What important behavior is not tested.
3. Whether error cases are covered.
4. Whether edge cases are missing.
5. Which additional tests would provide the most value.

The goal is not to maximize the number of tests.

The goal is to identify meaningful untested behavior.

For example, if an API accepts a quantity:

public IActionResult Calculate(int quantity)
{
    if (quantity <= 0)
        return BadRequest();

    return Ok(quantity * 10);
}

A useful review should consider:

quantity = 1
quantity = 0
quantity = -1

rather than simply recommending another test because the method is not "fully covered."

Security-Focused Pull-Request Reviews

Security-sensitive changes deserve a separate review pass.

You can ask the agent to focus specifically on:

Review this pull request for security issues.

Pay particular attention to:
- Authentication
- Authorization
- Input validation
- Injection risks
- Sensitive data exposure
- Secrets
- Logging
- File access
- External requests
- Unsafe deserialization

For example, this code should receive additional scrutiny:

var sql = $"SELECT * FROM Users WHERE Name = '{name}'";

A review should identify the SQL construction as a potential injection problem and recommend parameterized database access.

The same principle applies to logging:

logger.LogInformation("Payment request: {Request}", request);

If request contains sensitive customer or payment information, the logging decision needs to be reviewed.

AI can identify these patterns, but security findings should still be validated by developers or security specialists where appropriate.

Reviewing Architecture and Design

Not every pull-request problem is a syntax or logic bug.

Sometimes the implementation works but does not fit the application's architecture.

For example, a controller may directly access a database when the project consistently uses a service layer.

You can ask:

Review this pull request against the existing architecture.

Identify changes that:
- Bypass established application layers
- Duplicate existing services
- Introduce inconsistent dependency patterns
- Create unnecessary coupling
- Violate existing conventions

This type of review is valuable because the agent can compare the new implementation with patterns already present in the repository.

Reviewing a .NET Pull Request

Consider a simple service change:

public async Task<Order?> GetOrderAsync(int id)
{
    return await _context.Orders
        .FirstOrDefaultAsync(x => x.Id == id);
}

An AI-assisted review could investigate:

The important part is that these questions require repository context.

A code-generation model looking only at the method may not have enough information to answer them.

How to Ask Better Review Questions

The quality of an AI review depends heavily on the review request.

A vague prompt:

Review this PR.

is less useful than a structured request:

Review this pull request as a senior .NET developer.

Focus on:
1. Correctness
2. Security
3. Backward compatibility
4. Error handling
5. Test coverage
6. Performance
7. Consistency with the existing architecture

For each finding:
- Identify the affected file or area.
- Explain why it is a problem.
- Describe the likely impact.
- Suggest a practical fix.

Do not report style preferences unless they create a meaningful maintenance problem.

This gives the agent a defined review scope.

Review Findings Should Be Evidence-Based

One of the biggest risks of AI-assisted code review is false positives.

Suppose an agent says:

This database query may be slow.

That statement is not enough.

A useful finding should explain why.

For example:

The query loads all matching records before applying pagination.
For large result sets, this can increase database and application memory usage.
Apply pagination before materializing the query.

The second finding gives the developer something concrete to verify.

A good review should distinguish between:

These are not equivalent.

Reviewing the Pull Request Before Approval

A practical workflow can look like this:

1. Read the PR description

Understand the intended change.

2. Ask the Git Agent for a summary

Get an overview of the changed files and behavior.

3. Run a correctness review

Ask the agent to find concrete implementation issues.

4. Run a security review

Use a separate prompt focused on security.

5. Review tests

Check whether important behavior is covered.

6. Validate findings

Open the affected code and verify each important finding.

7. Run the test suite

Use the project's normal validation process.

For a .NET project:

dotnet restore
dotnet build
dotnet test

8. Complete the human review

The final approval should come from a developer who understands the change and its business context.

Comparison: Manual Review vs AI-Assisted Review

Area

Manual review

AI-assisted review

Repository understanding

Depends on reviewer

Can inspect repository context

Repetitive checks

Time-consuming

Faster

Business context

Strong when reviewer knows domain

Usually limited

Code pattern detection

Depends on experience

Can identify common patterns

Security review

Requires expertise

Useful as an additional pass

False positives

Usually lower when reviewer knows system

Can occur

Final decision

Human

Human should remain responsible

The two approaches work best together.

AI is useful for expanding the review surface. Human reviewers provide business context, architectural judgment, and final accountability.

Advantages of AI-Assisted Git Reviews

Disadvantages and Limitations

Common Mistakes

Asking for a Generic Review

"Review this PR" provides little direction.

Define what you want checked.

Treating Every Finding as a Bug

Not every AI observation represents a real defect.

Verify the code and context.

Ignoring Business Rules

An AI agent can understand code but may not know an undocumented business requirement.

Reviewing Only the Diff

Some defects are visible only when the changed code is considered together with its callers, configuration, database behavior, or tests.

Allowing AI to Become the Approval Gate

AI should assist the review process. It should not become the only reviewer for important production changes.

Best Practices

Keep Review Prompts Specific

Define the areas you want checked.

Separate Review Passes

Run different passes for correctness, security, testing, and architecture instead of asking one enormous question.

Ask for Evidence

Require findings to identify the relevant code and explain the reason for the concern.

Validate High-Risk Findings

Security, data-loss, concurrency, authentication, and authorization findings deserve manual verification.

Run Tests

AI analysis cannot replace compilation and automated tests.

Review the Business Impact

Technical correctness is only one part of a production change.

Troubleshooting AI Review Results

The Agent Reports Too Many Issues

Narrow the scope.

Ask it to report only high-confidence correctness or security issues.

The Agent Misses an Obvious Problem

Provide additional context.

For example:

Focus specifically on authorization. Check whether every path reaching this endpoint verifies that the current user is allowed to access the requested order.

A focused question often produces a more useful analysis.

The Suggestions Do Not Match the Project

Ask the agent to first identify existing patterns before recommending changes.

For example:

Before suggesting a refactoring, identify how similar functionality is implemented elsewhere in this repository.

This helps avoid introducing a pattern that conflicts with the existing architecture.

Summary

Visual Studio's AI-assisted Git workflow can make pull-request reviews more efficient by giving developers another way to inspect repository changes.

The most useful application is not simply asking an AI to find bugs. A Git Agent can be used to summarize a pull request, inspect related code, examine tests, look for security problems, and identify areas that deserve closer human attention.

The best workflow remains collaborative:

AI Analysis
    +
Repository Context
    +
Automated Tests
    +
Human Review
    =
Safer Pull Request Review

Developers should treat AI findings as review input rather than final decisions. The agent can reduce repetitive work and surface issues that might otherwise take longer to find, while human reviewers remain responsible for validating the findings and understanding the business and architectural context.

For teams already using AI-assisted development in Visual Studio, Git Agent-style workflows can turn AI from a code-generation tool into a broader development assistant that participates in the pull-request review process.