Why I created PyBotchi (v4.1.4)?

Hello Everyone, I'm the creator of PyBotchi, an intent-based AI Agent Orchestrator. In this post, I will discuss some key concepts why I created it. A little bit of background first. I'm a solutions architect with 10 years of experience as a software engineer. Most of my work are high throughput, high reliability, low cost and low latency services. This is while making it simple and readable to improve it's maintainabality. When I'm designing a system, I usually prioritize these concerns. You may assume this is my bias in relates to AI Agent building. I'm also Claude Certified Architect (Foundation) and I found that PyBotchi aligns almost identical to Anthropic's core agent recommendations. TL;DR: PyBotchi is an lightweight, async-first Python framework that uses nested Pydantic models and OOP inheritance to turn LLM intent detection into clean, deterministic business logic without the overhead of complex graph orchestration. Why I created PyBotchi? I really believed that traditional coding can already solved what client's need. The only limitations we have is how we read the input and how we show the output. In most cases in web services, your API use JSON, XML, etc with their respective specification/structure. Input Analogy Assume you have created a Books CRUD endpoints (FastAPI with Pydantic). Your create endpoint will have a define specifications for book creation to have a validation and avoid user errors. Most of the time you will also validates sessions and permissions which also included in the request. If you want your chat bot to support those, you just need add those endpoint as intent (tools). If your model tool selection are able to detect intents. You are more "close" to being deterministic. "Your services will have 50 endpoints or more. You will flood your tool selection call" In your frontend UI, you segregate panels/forms/inputs in their respective pages. You don't usually join multiple intent in a same page. Cluttered UI will make your UX confusin