# Agentic AI in Python: Zero to Production — The Full Series

> The complete Agentic AI in Python series — six parts that take you from a local LangGraph agent to a deployed, memory-keeping, tool-using, observable app. Start here.

*Source: https://www.infowok.com/agentic-ai-python-series/ · Sukhveer Kaur · Published June 20, 2026*

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This is the home base for **Agentic AI in Python: Zero to Production** — a six-part, build-along series. You start with a bare Python script and finish with an agent that reasons, calls real tools, remembers context, runs as a deployed service, and tells you when it breaks.

Each part picks up exactly where the last one left off, so the fastest path is to **start at Part 1 and work forward.**

## What you'll build

By the end of the series your agent can:

- **Think** — a LangGraph agent loop that reasons and calls tools (Part 1)
- **Serve** — wrapped in a FastAPI service, containerised, and deployed (Part 2)
- **Coordinate** — split work across a multi-agent structure (Part 3)
- **Remember** — persist context across turns with a real checkpointer (Part 4)
- **Act** — call live tools over an MCP client, with authentication (Part 5)
- **Be measured** — traced and scored against a fixed test set so regressions surface early (Part 6)

## The series, in order

1. **[Part 1 — Tools, StateGraph & Memory](/build-agentic-ai-app-python-part-1/)** — Build a working local agent in Python with LangGraph: tools, a StateGraph loop, and a SQLite checkpointer. This is the foundation every later part builds on.
2. **[Part 2 — FastAPI, Docker & Deploy](/build-agentic-ai-app-python-part-2/)** — Put the agent behind an HTTP API with FastAPI, containerise it with Docker, and deploy it so you can call it from anywhere.
3. **[Part 3 — Multi-Agent Systems](/build-agentic-ai-app-python-part-3/)** — Split the work across specialised agents that hand off to each other, and learn when multi-agent actually beats a single agent.
4. **[Part 4 — AI Agent Memory](/build-agentic-ai-app-python-part-4/)** — Give the agent durable memory so it remembers context across turns and sessions with a production checkpointer.
5. **[Part 5 — MCP Client & Real Tools](/build-agentic-ai-app-python-part-5/)** — Wire the agent to real tools over the Model Context Protocol, with authentication and basic guardrails against prompt injection.
6. **[Part 6 — Observability & Evals](/build-agentic-ai-app-python-part-6/)** — Trace every run and tool call with Pydantic Logfire, then score the agent against a fixed test set so you catch regressions before users do.

## How to use this series

- **New to agents?** Read [What Are AI Agents?](/what-are-ai-agents-complete-guide-2026/) first for the concepts, then come back to Part 1.
- **Just want a running agent?** Parts 1–5 are cumulative — follow them in order.
- **Already have an agent?** Jump straight to [Part 6](/build-agentic-ai-app-python-part-6/) to make it observable and testable.

Pick up at Part 1 below, and subscribe to the [RSS feed](/rss.xml) so new parts land in your reader.