Unit 1 of 9 · Beginner

The engine, the desk, and the filing cabinet

In one read

An agent has three plain parts. The model is the reasoning engine — the part that reads a situation and decides what to do. The context window is what the model can see and hold in mind right now: everything you have told it this session, the data it just fetched, its own recent steps. It is finite, like a desk — only so much fits at once, and when it fills, older items fall off. Memory is what survives between sessions: the filing cabinet the agent files things into and pulls back out later. The model is powerful but forgetful — on its own it retains nothing once the desk is cleared. So what an agent can reliably do depends less on how clever the model is than on what sits on its desk and in its cabinet at the moment it decides. Manage those two and you manage the agent.

Three parts, three plain pictures

Under the loop from the last unit, an agent has three parts worth naming. A simple analogy holds all three: an engine, a desk, and a filing cabinet.

  • The model — the engine. The model is the reasoning engine: the part trained to read a situation and produce a decision. It is where the intelligence lives.
  • The context window — the desk. The context window is everything the model can see and hold in mind right now: what you have told it this session, the data it just fetched, its own recent steps. Crucially it is finite, like a desk. Only so much fits at once, and when the desk fills, the oldest papers slide off the edge.
  • Memory — the filing cabinet. Memory is what persists between sessions: the cabinet the agent can file a note into today and retrieve tomorrow. Left to the model alone, there is no cabinet — clear the desk and it remembers nothing.

Why "smart but forgets" matters

This is the sentence to hold onto: the model is smart but forgets. A brand new session starts with an empty desk. Whatever the agent needs to reason well — your instructions, the relevant document, the state of the task — has to be on the desk at the moment it decides.

So the reliability of an agent turns less on raw model cleverness than on information logistics: what is on the desk, what is in the cabinet, and whether the right things are in reach when a decision is made. The next two units are about exactly that — how you brief it, and how it fetches what it needs.

See it in kestrel

Kestrel is built around this fact: it hands an agent a purpose-built "desk" for the markets — the current session, positions, and risk in view. Watch one on a generic instrument, no signup, no card:

npx kestrel.markets sim fomc-rate-decision-whipsaw

It prints a certified proof URL you can recompute yourself. Drop the kestrel.markets MCP server into your client and the desk is set up again next session — no account in between.

Recompute it

Every claim in this unit recomputes from a certified proof — no account, no card.

/proof/art_d29415f0cf502f4a218a9cba
Keep readingHow you tell an agent what to do

Agent twin: /education/agents-fundamentals/af-models-context-memory/content.md