Agentic AI Engineer Bootcamp: AI Agents, ReAct & RAG in C++

About this course

AI agents are exciting, but the real engineering challenge starts after the first impressive demo. Can you trace what the agent did, inspect the evidence it used, and control the tools it can call? Can you evaluate answer quality and decide when a human should approve the next step?

This course teaches agentic AI as an engineering discipline. You will build the core mechanics of AI agents in modern C++20, starting with a visible Predict, Reason, Act, Observe loop and gradually turning that loop into a production-style agent runtime. Instead of hiding the system behind a hosted API, every section uses local, deterministic C++ programs so you can see how state moves, how tools are selected, how retrieval changes answers, and how runtime decisions become reviewable.

The course is not a prompt collection. It is a systems-first path for engineers who want to understand how AI agents, ReAct, RAG, memory, tools, planning, safety, human approval, and observability fit together.

In this course, you will build and inspect:- A Predict, Reason, Act, Observe agent loop- A tool-using incident agent with routing, retries and traceable decisions- A ReAct advisor that connects reasoning, actions, observations and evidence-backed answers- A grounded RAG engine with document loading, chunking, retrieval, citations and quality checks- A real-world assistant over a larger local corpus- A memory-driven support agent with working memory, long-term facts, episodic recall, TTL and metrics- Multi-agent coordination flows with roles, messages, handoffs and ownership boundaries- Evaluation and safety harnesses for quality, reliability and reviewable behavior- OpsCopilot, a local GPT terminal assistant with RAG, memory, safety, commands, citations, reload, source inspection and runtime stats

You will learn how ReAct traces make agent behavior easier to follow, how RAG turns lo

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