DEV Community: Suzuki Yuto

🧠 Kaizen Agent Architecture — How Our AI Agent Improves Other Agents

Suzuki Yuto — Fri, 18 Jul 2025 22:25:25 +0000

At Kaizen Agent, we’re building something meta: an AI agent that automatically tests and improves other AI agents.

Today I want to share the architecture behind Kaizen Agent, and open it up for feedback from the community. If you're building LLM apps, agents, or dev tools—your input would mean a lot.

🧰 Why We Built Kaizen Agent

One of the biggest challenges in developing AI agents and LLM applications is non-determinism.

Even when an agent “works,” it might:

Fail silently with different inputs
Succeed one run but fail the next
Produce inconsistent behavior depending on state, memory, or context

This makes testing, debugging, and improving agents very time-consuming — especially when you need to test changes again and again.

So we built Kaizen Agent to automate this loop: generate tests, run them, analyze the results, fix problems, and repeat — until your agent improves.

🖼 Architecture Diagram

Here’s the system diagram that ties it all together — showing how config, agent logic, and the improvement loop interact:

📊 Note: Due to dev.to's image compression, click here to view the full resolution diagram for better clarity.

⚙️ Core Workflow: The Kaizen Agent Loop

Here are the five core steps our system runs, automatically:

[1] 🧪 Auto-Generate Test Data

Kaizen Agent creates a broad range of test cases based on your config — including edge cases, failure triggers, and boundary conditions.

[2] 🚀 Run All Test Cases

It executes every test on your current agent implementation and collects detailed outcomes.

[3] 📊 Analyze Test Results

We use an LLM-based evaluator to interpret outputs against your YAML-defined success criteria.

It identifies why specific tests failed.
The failed test analysis is stored in long-term memory, helping the system learn from past failures and avoid repeating the same mistakes.

[4] 🛠 Fix Code and Prompts

Kaizen Agent suggests and applies improvements not just to prompts, but also modifies your code:

It may add guardrails or new LLM calls.
It aims to eventually test different agent architectures and automatically compare them to select the best-performing one.

[5] 📤 Make a Pull Request

Once improvements are confirmed (no regressions, better metrics), the system generates a PR with all proposed changes.

This loop continues until your agent is reliably performing as intended.

🙏 What We’d Love Feedback On

We’re still early and experimenting. Your input would help shape this.

👇 We'd love to hear:

What kind of AI agents would you want to test with Kaizen Agent?
What extra features would make this more useful for you?
Are there specific debugging pain points we could solve better?

If you’ve got thoughts, ideas, or feature requests — drop a comment, open an issue, or DM me.

💡 Big Picture

We believe that as AI agents become more complex, testing and iteration tools will become essential.

Kaizen Agent is our attempt to automate the test–analyze–improve loop.

🔗 Links

GitHub: https://github.com/Kaizen-agent/kaizen-agent
Twitter/X: https://x.com/yuto_ai_agent

Free Places to Post Your Early Product — And It Actually Worked

Suzuki Yuto — Fri, 18 Jul 2025 05:21:51 +0000

Over the past couple of weeks, I launched Kaizen Agent, an open-source AI teammate that tests and improves LLM agents.

I didn’t use my personal network.

I didn’t pay for ads.

And I haven’t launched on Product Hunt yet.

Instead, I posted my Early Product across free public platforms — and surprisingly, it actually worked.

This post shares where I posted, how much traffic I got, and what worked best.

If you're building your own product, this might help you get early traction without spending a dollar.

📍 Where I Posted Kaizen Agent for Free

1. Reddit – r/mlops & more

Link: r/mlops post

Result: 97 views / 57 unique visitors

Notes: I also posted in a couple more subreddits.

Tips: Write like you're sharing an idea, not promoting. Reddit cares about authenticity.

2. Twitter (X) – #buildinginpublic

Link: My Tweet

Result: 114 views / 42 unique visitors

Tips: Post your tweet in communities like #buildinpublic. Also, replying to tweets that ask “What are you building?” or “Drop your projects below” can drive visibility and engagement. These replies often bring more profile visits than standalone tweets.

3. Hacker News – Show HN

Link: HN Post

Result: 67 views / 36 unique visitors

Tips: Use a “Show HN: [Tool] – What it does” format. Hacker News is great for dev feedback.

4. Daily.dev

Link: Kaizen Agent on Daily.dev

Result: 26 views / 18 unique visitors

Notes: I submitted this manually. It’s a clean, developer-focused platform that helped drive solid traffic.

5. ItsLaunched

Link: Kaizen Agent on ItsLaunched

Result: 4 views / 4 unique visitors

Tips: Super quick submission.

6. PeerPush

Link: Kaizen Agent on PeerPush

Result: 9 views / 2 unique visitors

Tips: Built for indie hackers. Worth a try for early exposure.

📊 Final Results (4 Weeks, No Personal Network)

Source	Total Views	Unique Visitors
Reddit	97	57
Twitter (X)	114	42
Hacker News	67	36
Daily.dev	26	18
PeerPush	9	2
ItsLaunched	4	4
Google Search	13	9
GitHub.com	361	6 (likely includes my own views)
Total	691	174

🗓️ This was over about 4 weeks — again, no personal network, no paid traffic, no Product Hunt.

💡 Why You Should Try This Before Product Hunt

If you're planning a Product Hunt launch, doing this beforehand helps you:

Validate interest and messaging
Collect real feedback
Build credibility and trust
Improve your GitHub or landing page
Start getting traction — for free

You don’t need to “go viral” — you just need real people engaging with your project.

💬 Know More Free Places?

I'd love to learn from others too.

Drop a comment if you know more free ways to promote your Early Product — I'll try them and update this post.

👉 Follow me on X

👉 Check out Kaizen Agent on GitHub

I built an AI agent that helps you improve your LLM apps — automatically.

Suzuki Yuto — Sun, 06 Jul 2025 16:06:58 +0000

Suzuki Yuto

Jul 4 '25

Tired of trial and error to fix your LLM app? I built a tool to automate it.

#ai #aiops #llm #opensource

Comments

3 min read

What I Did in the First 10 Days After Launching My Open-Source AI Tool (The Real Story)

Suzuki Yuto — Fri, 04 Jul 2025 18:35:05 +0000

Most launch stories you hear are flashy: "Launched on Hacker News, got 1,000 stars overnight."

This isn’t one of those stories.

This is a real one.

In the first 10 days of launching my open-source tool — Kaizen Agent
— I got:

⭐ 15 GitHub stars
🍴 3 forks
And 9 of those stars came from my engineering friends I personally messaged

But those early days were incredibly valuable — not because it went viral, but because the feedback I got helped me move forward fast.

😅 I almost didn’t launch

To be honest, I was a little hesitant to launch.

The onboarding process wasn’t polished. The tool wasn’t perfect. I thought,

“Should I wait until it feels more complete?”

But I decided to post anyway — just to see what happens.

And that’s when everything started moving.

📣 Where I launched

In the first few days, I:

Posted to Hacker News: https://news.ycombinator.com/submitted?id=yuto_1192
Shared on Reddit: https://www.reddit.com/r/AIAGENTSNEWS/comments/1lobzw8/tired_of_trial_and_error_to_improve_your_ai_agent/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button
Created a new Twitter account: @yuto_ai_agent
Sent it to some engineering friends

No major launch strategy — just shipped it and started talking about it.

🧠 The feedback that changed everything

After launching, I got a few important messages — from friends and Reddit comments — that really helped.

The key feedback:

“It’s cool, but I didn’t really know how to get started.”

That was 100% valid. My onboarding wasn’t clear. The README was dense. It wasn’t easy to try.

So I paused any further promotion and focused on making the product easier to use.

🔧 What I improved

Rewrote the README
- Made it simpler
- Added a dead-easy example
- Focused on clarity
Published to PyPI
- So people could run pip install kaizen-agent
- No more cloning and pip-editing
Launched a docs site
- Documentation here
- Added a proper walkthrough for YAML format and usage

📈 What changed

After improving the onboarding:

GitHub star conversion rate increased significantly
Strangers forked it

📊 Screenshots of traction

Here are two screenshots showing the traction from GitHub traffic and stars:

💡 What I learned

Launch early, even if it’s imperfect

As long as the core function works, feedback is worth more than polish.
README is your first impression

If people don’t understand it in 10 seconds, they won’t try.
Ask for feedback

Especially from AI developers working with LLMs or agents — it’s how I found direction.

🙏 Final thoughts

If you’re building an AI tool or LLM app, and wondering if it’s “ready” to share — launch it. Just make sure the core thing works.

Ask for feedback. Then improve from there.

If you're curious, here’s the project:

👉 https://github.com/Kaizen-agent/kaizen-agent

And if you work with LLMs or AI agents, I’d love your thoughts or feedback.

Thanks for reading!

— Yuto

Tired of trial and error to fix your LLM app? I built a tool to automate it.

Suzuki Yuto — Fri, 04 Jul 2025 17:57:14 +0000

Hi devs! 👋

I'm Yuto, and I want to share the story of why I built Kaizen Agent — an open-source CLI tool that tests, debugs, and auto-fixes LLM apps and agents.

This post is about why I built it, the pain that led me here, and how it works. If you're building with LLMs and tired of the trial-and-error cycle, I hope this resonates with you.

😤 The real pain behind building LLM apps

Over the past year, I’ve been working on LLM agents and applications as part of my startup and my PhD.

One thing I’ve realized is this:

Building LLM apps isn't that hard — but getting them to production-quality is brutally hard.

You can write a basic agent or prompt flow pretty quickly. But making it robust enough to actually use in production? That’s where it gets messy.

Here’s what I kept running into:

I’d write a prompt, test it… and get weird or inconsistent output.
I’d fix the prompt or logic, test again… and break something else.
I’d try to define test cases, run evaluations, and compare outputs manually — over and over.

Honestly, it felt like I was doing the same boring, manual steps repeatedly:

Write some test cases
Run the agent
Check the outputs manually
Fix the prompt/code
Repeat again and again

This manual cycle was killing my energy.

💡 The insight: LLM testing is different

That’s when something clicked:

LLMs are black boxes. You can't know if your change helps unless you actually test it.

Unlike traditional software, where you can reason through logic and expect consistent outputs, LLMs require a test-it-and-see approach.

You must:

Feed in test data
Evaluate outputs
Spot failure patterns
Iterate based on those observations

So I asked myself:

Why don’t we have tools optimized for this loop — for AI agents and LLM apps specifically?

We don’t just need unit tests or integration tests. We need feedback loops that help us improve LLM behavior.

🛠️ The idea: automate my own debugging process

That’s when I decided to build Kaizen Agent.

The idea was simple:

Define your test inputs, expected behavior, and evaluation logic in a YAML file
Run tests on your LLM app or agent
Detect failures and understand what went wrong
Suggest prompt/code fixes using another LLM
Re-run the tests automatically
(Optional) Open a pull request with the improved prompt/code

So instead of running tests manually and fixing things yourself, you can just run one CLI command — and let the agent debug itself.

🚀 The launch

Once the core functionality worked, I put it on GitHub and released the first version. The README was rough. There was no documentation yet. But it worked.

Since then, I’ve:

Improved the README with a super simple example
Created a full documentation site for better onboarding
Published to PyPI (pip install kaizen-agent) to make it easier to try

🙏 Final thoughts

If you’ve ever felt stuck in the loop of:

Prompt → test → tweak → test again…

and wished someone (or something) could help — I built this for you.

Check out Kaizen Agent on GitHub, and if it’s helpful, please give us a star ⭐ and share your feedback.

You can also follow me on X/Twitter: @yuto_ai_agent — I’d love to hear your thoughts or questions!

Thanks for reading!

— Yuto