Tool Use 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.

Capabilities and Routing

Sat, 08 Aug 2026 17:09:00 +0100

Every mechanism built so far has taken the action set as given. The action boundary validates a proposal against a fixed list of permitted action types. Candidate generation samples several proposals from the same list. The runtime-state chapter watches what happens after execution and decides whether to continue. All of them assume that somebody, somewhere, already decided which capabilities the policy could choose from.

So far we have treated that decision as an input. This chapter makes it explicit, because the capability surface changes the decision problem the policy has to solve and therefore belongs inside the agent architecture 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.