How to Build a Code Reviewer Agent
An AI agent for code review that takes your query, searches your code repository, and delivers an actionable summary.
Connectors and tools
GitHub
GitLab
Created by
xpander.ai
Built for
Developers, QA, and compliance teams needing fast, expert code reviews.
Security professionals seeking automated risk detection.
Any tech professional who wants to build AI agents to automate code analysis and compliance checks.
Challenge
Enterprise development and security teams often struggle to maintain comprehensive oversight across scattered repositories and documentation, leading to missed bugs, security vulnerabilities, and compliance gaps. By utilizing automation to handle the search and analysis process, this agent eliminates the bottleneck of manual scanning, allowing teams to generate actionable test plans and risk assessments in minutes rather than weeks, streamlining the path to production.
How the AI agent works
Below is how the agent works:
1. User query: The agent lets the user enter their code review or compliance enquiry request.
2. Code search: The agent searches your code repository for relevant code snippets or documentation matching the user’s query.
3. Code analysis and compliance review: The agent analyzes the retrieved code for programming errors or bugs, security issues, compliance risks, optimization opportunities and provides a structured diagnosis and specific recommendations.
Key benefits
- Accelerated production cycles: By automating the review process, development teams can significantly reduce the time spent on manual checks, speeding up deployment to production.
- Enhanced enterprise compliance: The agent ensures code adheres to strict regulatory frameworks and internal standards, reducing legal and security risks for enterprises.
- Unified knowledge orchestration: It connects disparate data sources—from GitHub to documentation—providing a centralized orchestration layer for comprehensive code insights.
- Consistent Best Practices: The agent enforces coding standards and best practices uniformly across the entire codebase, eliminating human error and subjectivity.
