Automated Code Review Agent

The Problem

Manual code review is time-consuming and inconsistent. Senior developers spend hours reviewing PRs, often missing subtle bugs or style violations due to fatigue. Teams need a way to automate the repetitive parts of code review while maintaining quality standards.

The Solution

Build an AI-powered code review agent on Flezi AgentBox that automatically analyzes pull requests, identifies potential bugs, enforces your team's coding standards, and suggests improvements — all before a human reviewer even looks at the code.

How It Works

Real Example: Live Output from Flezi AgentBox

This is actual output from the BMAD Code Reviewer agent running on Flezi AgentBox with Gemini, reviewing a function with a SQL injection vulnerability:

Input:

function getUserData(userId: string) {
  const query = `SELECT * FROM users WHERE id = ${userId}`;
  const result = db.execute(query);
  return result;
}

Agent Output (2.9 seconds, Gemini 2.0 Flash):

## Findings
### [CRITICAL] Security: SQL Injection Vulnerability
**File:** src/index.ts:2
**Issue:** Directly embedding `userId` into the SQL query string
allows for SQL injection.
**Fix:** Use parameterized queries or prepared statements.
 
### [LOW] Correctness: Missing Error Handling
**File:** src/index.ts:3
**Issue:** The code lacks error handling for the database query.
**Fix:** Implement try-catch blocks to gracefully handle errors.

Building This Agent in the Builder Lab

Phase 1: Discovery Canvas

Place sticky notes to define your agent's review scope:

| Note Category | Example Content | |---------------|----------------| | Goal (Blue) | Find security vulnerabilities, style violations, and bugs | | Risk (Red) | SQL injection, XSS, command injection | | Constraint (Amber) | Output must include file path and line number | | Persona (Pink) | Senior SE, ultra-succinct, analytical | | Metric (Green) | Severity rating per finding |

Phase 2: Logic Studio

Build a 4-node DAG:

1. Load Code Context (Service node) — Receives code input
2. Analyze Quality Facets (Function node) — Runs security, correctness, performance checks
3. Severity Filter (Gate node) — Sorts findings by CRITICAL to LOW
4. Review Report (Data node) — Outputs structured markdown

Phase 3: Output Terminal

The synthesis engine generates the agent blueprint with the persona config and DAG wired together. Click Publish to make it live on the marketplace.

Key Features

• Multi-language support — Review TypeScript, Python, Go, Rust, and more
• Custom rule sets — Define your team's specific standards
• Security scanning — Flag OWASP Top 10 vulnerabilities
• Performance hints — Identify N+1 queries, memory leaks, and inefficient algorithms
• Auto-fix suggestions — Generate corrected code snippets
• Structured output — Severity levels, file references, and fix code

Agent Configuration

name: "BMAD Code Reviewer"
persona:
  tone: "Analytical"
  expertise: "Code Quality & Security"
  style: "Ultra-succinct, file paths and line references"
llm:
  model: "gemini-2.0-flash"
  temperature: 0.3
  max_tokens: 4096
dag:
  nodes:
    - { id: "n1", type: "service", label: "Load Code Context" }
    - { id: "n2", type: "function", label: "Analyze Quality Facets" }
    - { id: "n3", type: "gate", label: "Severity Filter" }
    - { id: "n4", type: "data", label: "Review Report" }

Results

Teams using automated code review agents on Flezi AgentBox report:

• 60% faster PR review cycles
• 40% fewer bugs reaching production
• 2.9s average response time with Gemini 2.0 Flash
• Consistent enforcement of coding standards across the team