Agnetlearn; From first principles to reliable agents
Source
agentlearn.dev
Author
init0
Date
Context
AgentLearn connects building an AI agent with deciding whether it works reliably. Its free curriculum follows the system from a model proposing an action through application code executing it, observing the result and choosing the next step. Runnable JavaScript and interactive labs accompany lessons on those components.
The course also makes evaluation part of the build: its support-assistant capstone asks learners to connect the assistant's architecture to an evaluation plan. That gives beginners a concrete destination beyond making an agent respond: explaining why their support assistant is, or is not, ready to ship.
Terms in this piece · Glossary
context engineering — Deciding what an agent sees at each step — the discipline that replaced prompt engineering once context windows got large enough to fill badly.
tool use — A model's ability to call external functions — run code, search the web, edit files — instead of only generating text.
prompt injection — An attack that hides instructions in content an AI will read — a webpage, email, or document — tricking it into following the attacker instead of the user.
multi-agent — Using several AI agents on one problem — splitting work in parallel, checking each other, or filling different roles like planner and reviewer.
