Building Zero-Hallucination AI Code Review Agents on AWS & AST Parsing
Learn how DevPulse AI combines deterministic Abstract Syntax Tree (AST) parsing with containerized execution and LLM orchestration to build scalable, zero-hallucination code review infrastructure.
š Building Verified AI Code Review Agents
As open-source codebases and GitHub Pull Requests scale, engineering teams face real pressure reviewing code for security and quality issues. Most AI code review tools suggest fixes that are never actually tested, leaving developers to manually verify whether a "fix" really works.
DevPulse AI addresses this by combining static code analysis with sandboxed fix verification ā every generated patch is applied, re-scanned, and tested before being shown as safe.
āļø Architectural Overview
DevPulse AI operates a 3-agent pipeline:
- Security Sentinel Agent: Scans for hardcoded secrets (regex + Shannon entropy), SQL injection, and other CWE-mapped vulnerabilities.
- Quality & Architecture Agent: Evaluates cyclomatic complexity, resource leaks, and test coverage using AST-based parsing.
- Auto-Fix Agent: Generates unified diff patches for fixable issues, applies them in an isolated sandbox, re-scans, and runs tests before marking a fix "Verified." Issues that can't be safely auto-fixed (like complex refactors) are flagged for manual review instead of a fake fix.
š ļø Key Takeaways
- Deterministic scoring: The Code Health Score (0-100) is fully explainable, not an opaque AI guess.
- Honest limitations matter: A tool that admits what it can't safely fix is more trustworthy than one that overclaims.
- Real-world testing: Validated against real, large open-source repositories, not just a scripted demo.
Built By: Rafia Minhaj
GirlScript Summer of Code contributor | GeeksforGeeks Campus Lead at CIT Ranchi
š GitHub: https://github.com/rafiaminhaj/devpulse-ai
GirlScript Summer of Code contributor | GeeksforGeeks Campus Lead at CIT Ranchi
š GitHub: https://github.com/rafiaminhaj/devpulse-ai
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