Beyond Next.js: How to Build a Clean, Production-Grade AI Agent in Python from Scratch

After completing our 14-part Next.js implementation module, Post #27 kicks off Section C by translating our production agent guardrails into Python using Pydantic v2 and httpx.

Translating Zod to Pydantic v2

Instead of hand-writing brittle JSON Schema dictionaries in Python, define your tool inputs using Pydantic v2 BaseModel classes and generate the LLM schema automatically via .model_json_schema(). When the model emits a tool_use block, validate the arguments with .model_validate() and wrap any ValidationError inside a structured ToolResultEnvelope so the LLM can self-correct on the next iteration.

The Bounded Native Loop

Writing a 60-line bounded loop (max_steps = 5) with the official SDK gives you full visibility over stop_reason == 'tool_use', parallel tool_use_id matching, and token telemetry before we wrap this engine inside a FastAPI server in Post #28.

Read the full tutorial with complete Python source code on my blog: https://zahidhasantonmoy.vercel.app/blog/build-simple-ai-agent-python-guide