The "hello world" agent works on day one. The "we have paying customers and a pager" agent works on day 200. The gap is operational: observability, retries, idempotency, graceful shutdown.
These are the patterns we see in production Python agent codebases, and the failure modes they prevent.
"Memory" is doing a lot of work in agent literature. It can mean: chat history within a session, long-term semantic recall across sessions, persistent state for resumable runs, or all three. They are different problems with different solutions.
Here is the practical taxonomy and how to wire each one in Python.
If you have a Python codebase and an LLM provider, you can ship a working AI agent today. The hard part is no longer "can the model use a tool?". It is "how do I keep this thing reliable in production, with memory, streaming, and a UI."
This guide walks the full path: from a single-tool prototype to a deployed Python AI agent with persistent threads and a typed frontend client. Every snippet runs.