Building a Senior Software Engineer AI Agent for Code Reviews

I recently built a role-based AI agent designed to simulate the behaviour of a Senior Software Engineer during code reviews. The purpose of the project was to explore how structured AI prompting could be used to create a more specialized engineering assistant — one capable of reviewing code, identifying risks, enforcing best practices, and providing meaningful technical feedback.

What My AI Agent Does?

Rather than focusing on code generation, I wanted the system to behave more like an experienced reviewer in a pull request workflow. The AI agent was designed to evaluate:

• Code quality and maintainability
• Security vulnerabilities
• Performance concerns
• Naming conventions
• Documentation quality
• Accessibility compliance
• General engineering best practices

To test the system, I provided it with a JavaScript utility function that intentionally contained several common development issues, including:

• A SQL injection vulnerability
• Poor variable naming
• Inefficient nested looping
• Weak error handling
• Maintainability concerns

I then asked the AI to review the code and determine whether it was ready to be merged. One of the first issues the agent identified was the unsafe SQL query construction:

let q = "SELECT * FROM users WHERE id = '" + userId + "'";

The system correctly flagged this as a SQL injection risk and recommended replacing it with parameterized queries. It also identified several engineering concerns beyond security, including:

• Inconsistent naming conventions
• Poor scalability in the item filtering logic
• Lack of structured error handling
• Readability and maintainability issues

The final review concluded with “Status: Needs Major Revision”, along with recommendations for improving the implementation before merging.

How I built my AI Agent

What made the project particularly interesting was how much the quality of the responses depended on the role definition itself. The AI agent was built primarily through structured prompt engineering. I created a detailed role script that defined:

• The responsibilities of the AI reviewer
• The engineering standards it should enforce
• The communication style it should maintain
• The structure of the review responses
• The priority order for evaluating code quality

Security, correctness, reliability, accessibility, and maintainability were explicitly prioritized within the prompt instructions. I also included accessibility requirements as part of the review process. The agent checks for considerations such as semantic HTML usage, keyboard accessibility, ARIA labels, and inclusive UI practices where relevant.

To make the feedback more realistic and easier to follow, I structured the review output into sections:

1. Code Review Summary
2. Strengths
3. Issues Identified
4. Recommended Improvements
5. Best Practice Notes
6. Final Assessment

This helped the responses feel closer to an actual engineering review rather than a generic chat-bot response.

Takeaways

One of the key takeaways from this project was how effective AI systems become when given clearly scoped responsibilities and well-defined standards. The more specific the role definition became, the more consistent and technically accurate the outputs were.

This project was a useful exercise in combining software engineering principles with AI system design. It demonstrated how role-based AI agents can move beyond simple question-answer interactions and begin supporting more structured engineering workflows.

Iyinoluwa Aboyeji Co-Creation Hub CcHub Nigeria Attention Factory @AttentionFactory