Agentic Systems on AIBussin — AI applications, systems and books

What Is an Agent, Really?

Sat, 08 Aug 2026 15:40:00 +0100

This book is about building systems around models. Before we build planners, memory, tool routing, critics, search and verifiers, we need an answer to a question that turns out to be harder than it looks:

What is an agent?

The word is currently applied to almost everything: a single LLM call, a chatbot, a fixed pipeline, a tool-using loop, and any five model calls with class names ending in Agent. Those systems may all be useful. But when one word covers all of them, it stops telling us anything about the computation, and we lose the ability to say which mechanism is doing the work.

Introduction to LLM Agents

Mon, 01 Jan 0001 00:00:00 +0000

What Is an LLM Agent?

An LLM agent is a software system built around a language model that can pursue a goal through context, state, tool use, feedback, and repeated interaction.

The model is important, but it is not the whole agent. The larger system supplies the prompt, chooses what context the model sees, exposes tools, records state, retrieves memory, validates actions, handles errors, and decides when the work is finished. When people say that an agent “remembers,” “plans,” or “uses a tool,” the precise mechanism usually belongs to this surrounding runtime, not to the model by itself.

Agents From First Principles 00: What Is an Agent, Really?

Sat, 08 Aug 2026 15:40:00 +0100

What Is an Agent, Really?

This is the first post in Agents From First Principles.

It follows two earlier series.

In PyTorch: Zero to Hero, we worked upward from tensors, autograd and neural-network building blocks until we could build a small language model ourselves.

In Models From First Principles, we moved one level higher. We looked at how learned components can be composed into scorers, value models, policy heads, recurrent models, hierarchical models and compact recursive systems.

Codex Manager: Building a Prompt-State Runtime for Hackathon-Grade Code Optimization

Sat, 16 May 2026 20:15:59 +0100

TL;DR

Codex Manager uses AI to generate code as an artifact, then tests that artifact, diagnoses what happened, and repairs the prompt state that produced it. The code is not the thing being optimized directly. The prompt state is.

Summary

Humanity’s Last Hackathon framed the challenge as a test of context, not code: the task was hard enough that the real question was not whether someone could hand-write one clever kernel, but whether they could build a system that used AI effectively under changing constraints.