** Separate registration is required for CppCon Academy Classes. **Class PageRemember writing a compiler in university? Not a real compiler, but something that handled a subset of a language and generated naive code. You’d never ship it. But decades later, you still understand how compilers work because you built one.
That’s what we’re doing here. In one intensive day, you will build a working AI coding agent in C++.
Not Claude Code. Not Cursor. A simple thing you fully understand, that talks to an LLM, defines tools, executes them, and runs the same agentic loop that powers every AI coding assistant on the market.
This workshop emerged from my own AI/C++ journey. I watched AI stumble through C++ like a freshman who skipped the first three weeks of class, until I discovered the problem wasn’t the models, it was me. The breakthrough came from understanding what’s actually happening under the hood.
In this workshop, you will write a fully functioning agent harness in C++. Like those university compilers, it will not be production ready, but will serve as a laboratory for learning how generative AI large language models can be used as C++ programmers.
By end of day, your agent will read code, write code, compile it, fix its own errors, and even modify its own source code to add new capabilities.
You won’t ship this agent. But you’ll understand what Claude Code and Cursor are actually doing. When they break, you’ll know why. When new tools emerge, you’ll evaluate them with comprehension instead of hype.
What You’ll Build:
A C++ program that talks to an LLM API
System prompts that shape model behavior
Tool definitions (read_file, write_file, run_command)
A working agentic loop
An agent capable of modifying its own source
What You’ll Understand:
Why the same model acts completely different with different prompts
What “tool calling” actually means (spoiler: you do all the work)
How context windows work and why you send the whole conversation every time
What “turns” are and how the conversation builds up
Why agents sometimes go off the rails and how to prevent it
Format: Lab-heavy. Students build; instructor guides. Starter code provided for API plumbing.
