The Multi-Agent Architecture: How Mesrai Uses Specialized AI Reviewers
Most AI code review tools work the same way: they send your code to a large language model (LLM) and hope for the best. While this can catch basic typos, it fails to understand the complex architectural relationships and business logic that define professional software.
At Mesrai, we took a different approach. We built a Multi-Agent Architecture that mimics how a senior engineering team reviews a pull request.
Why One Model Isn't Enough
A single AI model is like a general practitioner—they know a bit of everything but aren't experts in anything. When you're shipping mission-critical code, you don't need a generalist; you need a team of specialists.
Mesrai's architecture breaks the review process into 6 distinct stages, powered by 7 specialized agents.
The 6-Stage Review Journey
1. Smart Context Gathering (0-15s)
Before a single line is reviewed, our Context Agent fetches the PR diff, commit history, and—crucially—the repository structure. It builds a mental map of your codebase so it knows that a change in user.service.ts might affect auth.middleware.ts.
2. Advanced Code Analysis (15-30s)
We don't just read text; we parse code. Our AST Agent converts your code into an Abstract Syntax Tree. This allows us to perform "Semantic Chunking," breaking your code into meaningful blocks while preserving the architectural context that traditional AI tools lose.
3. Multi-Agent AI Review (30-90s)
This is the heart of Mesrai. We deploy 7 specialized agents that analyze your code in parallel, each from a different angle:
- ▹🛡️ Security Agent: Performs taint analysis to track unvalidated user input and identifies OWASP vulnerabilities.
- ▹✅ Quality Agent: Enforces clean code principles, DRY, and SOLID patterns.
- ▹⚡ Performance Agent: Identifies N+1 queries, memory leaks, and inefficient loops.
- ▹🏗️ Architecture Agent: Checks for circular dependencies and violations of your project's layering rules.
- ▹🎯 Business Logic Agent: Analyzes if the implementation matches the stated intent of the PR.
- ▹🌿 Testing Agent: Reviews test coverage and—more importantly—the quality and validity of those tests.
- ▹💻 Language Expert: Deeply understands the specific idioms and best practices of your chosen language (TypeScript, Go, Rust, etc.).
4. Intelligent Filtering (90-105s)
Raw AI output is noisy. Our Filtering Agent performs "Cross-Agent Verification." If the Security Agent finds a possible issue, the Filtering Agent checks it against the Architecture and Business Logic agents to eliminate false positives. We only show you what actually matters.
5. Actionable Feedback (105-115s)
We don't just give you a report; we give you a review. Our Feedback Agent generates inline GitHub/GitLab comments, complete with code suggestions that you can apply with a single click. It explains the why and the impact, not just the what.
6. Continuous Learning (115-120s)
Every time you dismiss a comment or apply a suggestion, our Learning Agent takes note. It adapts to your team's specific standards and "tribal knowledge," ensuring that future reviews are even more accurate.
The Results: 120 Seconds to Excellence
By using specialized agents instead of a single model, Mesrai achieves:
- ▹Lower False Positives: Cross-verification ensures high-signal feedback.
- ▹Architectural Awareness: We understand your system, not just your syntax.
- ▹Speed: Parallel processing means we can do a "Senior Level" review in about 2 minutes.
- ▹Cost Efficiency: You save 70% compared to traditional manual review time.
Experience the Multi-Agent Difference
Software is too complex for general-purpose AI. Your team deserves a review system that understands the nuances of your architecture and the rigor of your security requirements.