AI Agent Engineering

Who this course is for

If you've built a single-agent chatbot or a basic tool-calling loop and want to know how that becomes a production system — multiple agents coordinating, memory that persists across sessions, and guardrails that catch failures before a user does — this is the next step. It builds on the AI Fundamentals and Backend Engineer roadmaps, so you should already be comfortable calling an LLM API and building a basic backend service before starting.

What you'll actually build

The course is structured around five real engineering problems, in order: architecting an agent's planning loop and memory (ReAct-style reasoning, short-term vs. long-term memory), implementing tool/function-calling safely at scale, orchestrating multiple agents working together with a framework like LangGraph or AutoGen, adding evaluation suites and guardrails before anything ships to a real user, and deploying and monitoring the result in production. Each module pairs a concepts lesson with a hands-on build, so you're never just reading — you're extending the same system module over module.

The capstone

You finish by shipping a multi-agent customer-support system with guardrails and monitoring — the kind of project meant to go directly into a portfolio, not a toy demo. The goal is something you could walk an interviewer through line by line and explain every design decision in.

Why this matters right now

Multi-agent systems are one of the fastest-moving parts of applied AI, and also one of the easiest to get wrong — ungoverned tool access and missing guardrails are the most common reasons agent systems fail in production, not model quality. This course is built specifically around avoiding that: every module includes the safety/evaluation side, not just the "make it work" side.