Building AI Agents with Python

Give an AI Hands — Safely

A chatbot answers from memory. A RAG system answers from your documents. An agent goes further: give it a set of tools and it decides which to call, reads the results, and loops until it can answer — or act. That’s how an AI checks a service, reads a log, scrapes an API, and proposes a fix.

In this course you build opsbot, a real DevOps assistant agent in Python, from scratch with nothing but requests. It searches your runbooks (the RAG from the last course, dropped in as a tool), inspects live system state, fetches a live price from an API, and — behind a strict allowlist + confirmation + dry-run safety gate — can actually restart a service. You run it on your own machine against the free Mistral (LeChat) API.

What You’ll Build & Learn

• The one pattern behind every agent framework: the tool-calling loop
• Design tools the model actually uses — schemas, descriptions, a registry, and clean dispatch
• Handle tool errors and timeouts as data the agent can recover from, not crashes
• Give the agent read-only eyes on the system (status, logs, config) and wire your RAG in as a tool
• Build the safety spine: an allowlist, dry-run, and human confirmation before anything changes
• Assemble it into an interactive CLI and run a real incident — diagnose, propose, confirm, fix

How You’ll Learn

This is a build-on-your-machine project, not a lecture:

• Build one real agent — opsbot — module by module
• Lock in each concept with a quick quiz
• Download the complete, tested solution code for every step
• Everything runs locally against the free Mistral API — no GPU, no paid tooling

Where This Fits Your Journey

This is the step where your AI gets hands. On the BytExplorer AI-Assisted Developer path it follows Retrieval-Augmented Generation with Python — your RAG becomes one of the agent’s tools — and it leads into Build a Custom MCP Server with Python, where opsbot’s tools become a server any AI client can use.

Comfortable with Python and curious how agents really work under the hood? You’re ready. By the end you’ll have built an agent that acts on real systems — and the judgment to let it do so safely.

Ready to give your AI hands? Jump in.