Overview
A chatbot answers questions; an agent pursues goals — the difference is a loop, and by the end of this day you'll have built one and understood every line. You'll write system prompts as job descriptions (role, tools, goals, constraints, workflows), wire up the ReACT loop so the model decides its own next move, and give agents real tools and memory with RAG (FAISS, pgvector) — and see that it's search, not magic. Then you'll build your own agent framework in about 140 lines, so every SDK stops being a black box, and orchestrate a small swarm of agents that outwork you. No hand-waving: you leave able to build agents that think, act, and learn, from scratch.
Who this is for & what you'll need
Where This Class Leads
What you leave able to do
- construct a retrieval system
- construct an agent that uses tools
- govern a swarm by information hierarchy
- re architect an agent as a stateless reducer
- reconcile agents from different toolchains into one run
- select among agent architectures
- synthesise a self extending agent behind a review gate
How you show it. A diff the agent authored to its own prompt or toolset, the benchmark run that accepted it, and a logged rejection with the reason recorded.
It sits on 3 paths
🤖 Build production AI systems from prompt to deployment. 6 of 12 after Claude Model Context Protocol · before Prompt Engineering 🧑💼 Founder with an AI-first company. 6 of 7 after Context Engineering · before AI Security: Governance, Standards and Safety Cases 🧙 Build strange systems that change how people think. 4 of 8 after The Interactive Fiction Intensive · before Automate Your Email: No Code AI AutomationAbout 67 hours of practice sit behind this one. See what your hours buy.
Join Us
- Your seat in the class
- Lifetime access to materials
- Sponsors 1 scholarship seat for someone else
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Contact us: liz@themultiverse.school