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Introduction to Kaizen Agent

Test, debug, and improve your AI agents automatically. Kaizen Agent runs your agents, analyzes failures, and fixes code and prompts using AI.

What is Kaizen Agent?

Kaizen Agent acts as an AI debugging engineer that continuously tests, analyzes, and improves your AI agents and LLM applications. Instead of manually writing test cases and debugging failures, you simply:

  1. Define your test inputs and evaluation criteria in YAML
  2. Run kaizen test-all --auto-fix
  3. Let Kaizen automatically test, analyze failures, and improve your code

Watch Kaizen Agent in Action

Watch the full demo video

How It Works

Kaizen Agent Architecture

Kaizen Agent works by:

  1. Running your AI agent with various test inputs
  2. Analyzing the results using AI-powered evaluation
  3. Identifying issues in code, prompts, or logic
  4. Automatically fixing problems by improving prompts and code
  5. Re-testing to ensure improvements work

When to Use Kaizen Agent

Kaizen Agent is most valuable during the development phase of your AI agents, right after you've written the initial code but before deployment.

Perfect Timing: Pre-Deployment Testing & Tuning

After writing your agent code, you typically need to:

  • Test with various inputs to ensure reliability
  • Tweak prompts for better performance
  • Debug edge cases and failure scenarios
  • Optimize code based on test results

Kaizen Agent automates this entire process.

Ideal Use Cases

  • 🔄 Iterative Development: Test and improve agents during development cycles
  • 🚀 Pre-Deployment Validation: Ensure your agent works reliably before going live
  • 🐛 Bug Detection: Catch and fix issues you might miss with manual testing
  • 📈 Performance Optimization: Continuously improve prompts and code based on test results
  • 🛡️ Quality Assurance: Maintain high standards as your agent evolves

When NOT to Use

  • Production environments - Kaizen is for development/testing, not live systems
  • Simple, stable agents - If your agent is already working perfectly, you might not need it
  • Non-AI applications - Kaizen is specifically designed for AI agents and LLM applications

Key Benefits

🎯 No Test Code Required

Kaizen Agent uses YAML configuration instead of traditional test files:

  • ❌ Traditional approach: Write test files with unittest, pytest, or jest
  • ✅ Kaizen approach: Define tests in YAML - no test code needed!

🤖 AI-Powered Testing

  • Automatically generates test cases based on your agent's purpose
  • Uses AI to evaluate responses for quality, accuracy, and relevance
  • Identifies edge cases you might miss

🔧 Automatic Fixes

  • Improves prompts based on test failures
  • Fixes code issues automatically
  • Creates pull requests with improvements

📊 Detailed Analytics

  • Comprehensive test reports
  • Before/after comparisons
  • Performance metrics and trends

Get Started

Ready to try Kaizen Agent? Check out our Quick Start Guide to get up and running in minutes!

Community & Support

💬 Questions? Need help? Join our Discord community to ask questions, share your experiences, and get support from other developers using Kaizen Agent!

Open Source

Kaizen Agent is open source and available on GitHub. Check out the repository for source code, issues, and contributions.

🧠 Traditional Software Engineering vs. AI Agent Development

🛠 Traditional Software Engineering

  • You write deterministic code.
  • Then you write test code (e.g., unit tests, integration tests).
  • You run the tests to check pass/fail status.
  • If a test fails, you debug the logic, fix the code, and re-run the tests.

🔁 This is a structured, predictable feedback loop.


🤖 AI Agent / LLM Application Development

  • You build non-deterministic agents using prompts and LLM calls.
  • You can't write traditional test code — behavior varies.
  • Instead, you:
    1. Prepare a test dataset (inputs + expected outputs)
    2. Manually run the agent
    3. Evaluate the outputs yourself
    4. Tweak the prompt or agent logic
    5. Repeat

❌ This is time-consuming and subjective — like debugging a black box.


🔧 Kaizen Agent: Your AI Debugging Engineer

Kaizen Agent automates the test-and-improve loop, acting like a reinforcement learning system for AI agents.

  • Define test inputs, expected outputs, and evaluation criteria in YAML.
  • Kaizen runs your agent and evaluates the result using LLMs.
  • If the result fails:
    • It auto-fixes the code or prompt.
    • Re-runs the test until it passes.
  • (Optionally) creates a pull request with the improvements.

✅ Summary Comparison

Traditional SoftwareAI Agent DevelopmentKaizen Agent Workflow
Code TypeDeterministic logicNon-deterministic (prompt-based)Prompt + code (LLM-driven)
Testing MethodUnit testsManual test datasetsYAML-defined + auto-eval
EvaluationPass/FailSubjective human reviewLLM-based criteria scoring
Feedback LoopManual fix + re-runManual tweak + re-runAuto-fix + auto-retry
Automation LevelHighLowVery High

Diagram: Testing Workflows — Traditional vs AI Agents vs Kaizen Agent (Insert visual diagram here)