Notes on Agentic AI – A text-first guide for practicing engineers
Source
github.com
Author
tbaadm
Date
Context
Agentixit teaches an AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → as a loop that ordinary software must manage: give a model the current situation, interpret its proposed action, run the selected tool and return the result for another decision. Its engineering guide pairs explanations with small code examples, making the boundary between model decisions and program execution visible.
That structure gives experienced programmers a concrete starting point for understanding agents. They can follow how information and control move through the loop, then connect those mechanics to the guide's examples of validation, evalA repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.Full definition → and execution control.
Terms in this piece · Glossary
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.
RAG — Retrieval-augmented generation — fetching relevant documents first and pasting them into the model's context so it answers from your data instead of memory.
multi-agent — Using several AI agents on one problem — splitting work in parallel, checking each other, or filling different roles like planner and reviewer.
