2. Anatomy of an Agent - by Stephen Gruppetta

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Agents Unpacked<br>2. Anatomy of an Agent<br>Authors: Priya & Stephen

Stephen Gruppetta<br>Jun 21, 2026

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Read Stephen's Preface to Agents Unpacked if you're new here.

You have used a large language model. You know the deal: a careful prompt gets a careful answer. A vague prompt gets a vague one. And the model itself does not keep anything from one conversation to the next, unless something external is holding that context for it.<br>Agents work differently. They have parts that do things a plain LLM does not. These parts are what make an agent an agent. It is not just the model underneath. It is the structure built around it that gives the system its abilities to persist, act, and keep going.<br>Understanding this structure is the second major shift in this series. The first shift is seeing that a chatbot can give you a good answer without finishing the job, because it stops after responding. The second shift is seeing that an agent is not a smarter model. It is a model placed inside a structure that gives it something to act with and somewhere to keep what it has done.<br>The Agent Formula

Most agents share the same basic parts:<br>A model (the LLM): the reasoning engine that understands language and decides what to do

Instructions: what tells the agent who it is, what it is for, and what ‘good’ looks like

Memory: a workspace or store that holds what has happened so far

Tools: capabilities the agent can call on to do things beyond generating text

An execution loop: the cycle of observing, deciding, acting, and checking

Different platforms package these differently. Some call memory “context,” some call tools “plugins” or “capabilities,” and some merge instructions and tools into a single configuration layer. But the parts are the same. An agent is not a single thing. It is a system, and each part matters.<br>Stephen: Don’t LLMs also have memory since they remember what happened earlier in the conversation? How’s this different?

Here is one distinction worth getting clear early: the context window and memory are not the same thing. The context window is the working space an LLM uses during a single session. It holds the conversation so far and gets loaded fresh every time the model gets a chance to speak. Memory, by contrast, is information stored outside the model, maintained by the system, and available across sessions and steps. We will come back to this.<br>An agent needs all its components:<br>Agent = Model + Instructions + Memory + Tools + Execution Loop

Leave any one of these out and the system changes behaviour in ways that matter. We will look at each piece in turn.

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What the Model Does and What It Doesn’t Do

The model is the reasoning core. It reads your request, figures out what to do, and decides what to say back. It gets the most attention because it is the part that generates language.<br>But a model on its own is like a brilliant mind with no hands and no memory of its own. It can think. It cannot act. It cannot remember what happened five minutes ago unless something explicit carries that information forward.<br>Stephen: Wait a second. You say the model doesn’t remember what happened five minutes earlier. But when I use an LLM, it does seem to remember what happened earlier in the conversation.

Here is what is actually happening. When an LLM appears to remember earlier in a conversation, it is not the model itself that is remembering. The context window is carrying all the earlier messages along with your new message, every time you send something. The model sees the full conversation again and generates a response that fits what came before. That is not memory in the model. That is the system feeding the model a transcript.<br>This trips up almost everyone when they start using agents. The model generates text. The rest of the system decides what to do with that text and whether to act on it.<br>A better model helps. It reasons more clearly, follows instructions more faithfully, and handles edge cases better. But dropping a smarter model into an agent that is missing a working execution loop will not make it an agent. You need the other parts too.<br>Instructions: The Agent’s Direction

Instructions tell the agent what it is supposed to do and how to behave. Some systems call these system prompts. Others call them agent definitions or behavioural instructions. The name does not matter. What matters is that they are the layer that tells the model why it exists, who it is helping, and what ‘good’ looks like for the task at hand.<br>Good instructions do not make an agent smarter. They make it more focused. They give it a frame for every decision: what to prioritise, what to avoid, when to ask for help, how to present its output.<br>Stephen: Are these what are often called ‘skills’, or are skills something else...