AI Review Agent


Code reviews are one of the most important parts of modern software development. They help teams catch bugs early, improve maintainability, enforce coding standards, and improve overall software quality.

However, traditional code reviews also consume significant developer time. Reviewing every pull request manually becomes difficult in large repositories or fast-moving teams.

With the rise of Large Language Models (LLMs), we can now automate a large portion of the review process by building an AI-powered code review agent.

In this article, we’ll explore how to build a lightweight AI code reviewer in C# that:

The goal is not to replace developers, but to provide faster and smarter review assistance before human review begins.

Why Git Diff Is the Right Approach

One common mistake when integrating AI into code review systems is sending the entire repository to the LLM.

This causes several problems:

Instead, modern AI review systems focus primarily on Git diffs.

A Git diff contains:

This keeps the review focused only on recent changes.

Example:

git diff main...HEAD

Or:

git diff HEAD~1 HEAD

This approach is scalable, faster, and far more cost-effective.

High-Level Architecture

The architecture of the AI review agent is straightforward.

Developer Changes Code
        ↓
Git Diff Extraction
        ↓
Prompt Builder
        ↓
LLM API
        ↓
AI Review Response
        ↓
VS Code / PR Comments / CLI

The system can be integrated into:

Solution Flow

Here’s the typical workflow:

  1. Developer modifies code

  2. System extracts Git diff

  3. Optional context files are collected

  4. Prompt is generated

  5. Diff is sent to the LLM

  6. AI analyzes the changes

  7. Review suggestions are returned

  8. Feedback is shown to the developer

Step 1 - Extract Git Diff in C#

The first step is to retrieve changed code from Git.

Example:

using System.Diagnostics;

public static class GitHelper
{
    public static string GetGitDiff()
    {
        var process = new Process
        {
            StartInfo = new ProcessStartInfo
            {
                FileName = "git",
                Arguments = "diff HEAD~1 HEAD",
                RedirectStandardOutput = true,
                UseShellExecute = false,
                CreateNoWindow = true
            }
        };

        process.Start();

        string diff = process.StandardOutput.ReadToEnd();

        process.WaitForExit();

        return diff;
    }
}

This captures the latest commit changes.

You can also compare against:

Step 2 - Build the AI Prompt

Once the diff is collected, we build a prompt for the LLM.

A good prompt is critical for high-quality reviews.

Example:

public static string BuildPrompt(string diff)
{
    return $@"
You are a senior C# code reviewer.

Review ONLY the changed code.

Focus on:
- Bugs
- Security vulnerabilities
- Performance issues
- Null reference risks
- Maintainability
- Best practices

Return concise actionable feedback.

Git Diff:
{diff}
";
}

The most important rule:

Keep the prompt focused on changed code only.

Step 3 - Send Request to LLM

You can use:

Below is a simplified OpenAI example using HttpClient.

using System.Net.Http.Headers;
using System.Text;
using System.Text.Json;

public class AiReviewService
{
    private readonly HttpClient _httpClient;

    public AiReviewService(HttpClient httpClient)
    {
        _httpClient = httpClient;
    }

    public async Task<string> ReviewCodeAsync(string prompt)
    {
        var request = new
        {
            model = "gpt-4.1",
            messages = new[]
            {
                new
                {
                    role = "user",
                    content = prompt
                }
            }
        };

        var json = JsonSerializer.Serialize(request);

        var content = new StringContent(json, Encoding.UTF8, "application/json");

        _httpClient.DefaultRequestHeaders.Authorization =
            new AuthenticationHeaderValue("Bearer", "YOUR_API_KEY");

        var response = await _httpClient.PostAsync(
            "https://api.openai.com/v1/chat/completions",
            content);

        response.EnsureSuccessStatusCode();

        return await response.Content.ReadAsStringAsync();
    }
}

Step 4 - Parse AI Response

The LLM can return:

Structured JSON is preferred for automation.

Example response:

{
  "severity": "High",
  "file": "AuthService.cs",
  "line": 42,
  "issue": "Possible null reference exception",
  "suggestion": "Add null validation before accessing User object"
}

This enables:

Step 5 - Show Feedback to Developers

The final output can be displayed in multiple ways:

IntegrationExample
VS Code ExtensionInline review comments
CLI ToolConsole warnings
GitHub PRAutomated review comments
Azure DevOpsPull request feedback
Web DashboardReview summaries

Adding Smart Context

Diff-only reviews work well, but adding limited context improves accuracy.

Good context examples:

Avoid sending:

Best Practices for AI Code Review

1. Keep Reviews Incremental

Review only changed code.

Avoid full-repository scans unless absolutely necessary.

2. Limit Token Usage

Large prompts reduce quality and increase cost.

Smaller focused prompts perform better.

3. Use Structured Responses

JSON output is easier to integrate into developer tools.

4. Add Severity Levels

Categorize issues:

This improves developer experience.

5. Cache Repeated Reviews

Avoid re-reviewing unchanged diffs.

This reduces API costs significantly.

Advanced Enhancements

Once the basic version works, you can expand the platform with advanced capabilities.

Semantic Code Context

Retrieve related files automatically using embeddings or vector search.

Multi-Agent Reviews

Use specialized reviewers:

Local LLM Support

Use Ollama or local models for:

Pull Request Automation

Automatically trigger reviews during CI/CD pipelines.

Example platforms:

Common Challenges

Hallucinated Issues

LLMs sometimes report incorrect issues.

Solution:

Large Pull Requests

Huge diffs reduce review quality.

Solution:

Sensitive Code Exposure

Sending source code to external APIs may violate compliance requirements.

Solution:

Final Thoughts

AI-assisted code review is rapidly becoming a standard part of modern development workflows.

The most effective systems are surprisingly simple:

  1. Extract Git diff

  2. Build focused prompts

  3. Send to LLM

  4. Show actionable feedback

The key insight is:

Review the change, not the entire repository.

By combining Git diffs with LLMs, developers can build fast, scalable, and intelligent review systems directly inside existing development workflows.

Whether integrated into VS Code, Azure DevOps, or GitHub pull requests, AI code review agents can significantly improve developer productivity while reducing manual review effort.

The future of code review will likely be collaborative:

And C# provides an excellent ecosystem for building these tools.