Build Your First AI Agent Pipeline: A Practical Framework That Actually Ships

What We Are Building By the end of this tutorial, you will have a working multi-stage AI agent pipeline in Python — one where each agent has typed inputs, typed outputs, validation, and retry logic. No frameworks. No LangChain. Just clean code you actually understand. I call this approach DEVO : Decompose, Execute, Validate, Orchestrate. It is the pattern I use in every project that involves LLMs, and it is the difference between a demo that impresses and a product that ships. Here is the minimal setup to get this working. Prerequisites Python 3.11+ An OpenAI or Anthropic API key pydantic installed ( pip install pydantic ) Your LLM provider's SDK ( pip install anthropic or pip install openai ) Basic familiarity with async/await in Python That is it. No orchestration frameworks. No vector databases. No 47-dependency toolchain. Step 1: Understand the Agent Unit The atomic building block of this framework is the Agent Unit — a single, scoped AI call with a clear contract. Think of it like