When people struggle to design agent systems, it is often because they don't have a clear mental model for what the components actually do. Here is one that I find useful.
The Mental Model
- The LLM is the CPU. It is the processing unit. It takes inputs, applies reasoning, and produces outputs. It doesn't store state between calls — it just processes what you give it.
- The context window is RAM. It is working memory. Whatever is in the context window is what the model can reason over. Fast, limited, and gone when the session ends.
- Files, databases, embeddings, and documents are disk. They are persistent storage. They hold information across sessions, at scale, and outside the model's active attention. The model can't reason over disk directly — it has to load things into RAM first.
- Tools are external programs. The model can call them, receive their outputs, and incorporate those outputs into its reasoning. The tool does the work that the model shouldn't do inline — running a query, calling an API, doing a calculation, reading a file.
What the Agent's Job Actually Is
The agent's job is not to "know everything." No model is trained on your internal data. No context window is large enough to hold your entire knowledge base. That is not how this works.
The agent's job is to:
- Pull the right information from disk into working memory (retrieval)
- Call the right tools when the task requires external computation or data
- Use feedback from tool outputs to continue reasoning
- Decide when the task is complete and produce an output
This is a coordination job more than a knowledge job. The model is orchestrating information flow, not memorizing facts.
Why Bigger Context Windows Don't Solve the Problem
A common mistake: if the agent isn't reasoning well, give it a bigger context window. Load in more documents. Pass in more data. Surely more information helps.
It doesn't, reliably. And this is where the RAM analogy becomes useful.
More RAM helps — but bad memory management still creates bad systems. If you fill the context window with irrelevant information, the model has to process all of it. Important signals compete with noise. The model loses track of what matters. Performance degrades, often in ways that are hard to diagnose because the output still looks plausible.
The discipline is not "load everything." It is "load the right things at the right time." That requires knowing:
- What the model actually needs at this step
- What can stay on disk until it's needed
- What can be summarized rather than included verbatim
- What should be handled by a tool rather than loaded into context
The Practical Implication
The future skill in AI product development is not prompting. It is context architecture — designing the information flow so the model always has what it needs and never has what it doesn't.
That means building retrieval systems that fetch the right chunks, not everything. It means building memory systems that persist what matters across turns, not the full conversation. It means defining tool boundaries precisely so the model knows when to call a tool versus when to reason inline.
The teams that build reliable agents are not the ones with the best prompts. They are the ones who have thought carefully about what goes into RAM, when, and why.