What makes an AI agent different from an LLM?
An LLM answers; an agent does. The difference is four things bolted around the model: multiple models orchestrated together, memory and context, tools it can call, and a layer of checks running the whole time. You're not surfacing a model to your user, you're surfacing a software system — the model is one part of it. When someone says 'we built an agent,' the real question isn't how good the model is, it's what system they built around it.
Orchestration, not one model
A demo uses a single model. A real agent runs a combination — small ones, large ones, some fine-tuned, some open, some proprietary — each picked for what it’s good at. The job stops being “prompt the model” and becomes “coordinate the system.”
Memory and context
The agent needs access to your data, your history, and the right information at the right moment. Without it, you have a clever autocomplete. With it, you have something that can actually work on your problem.
Tools it can call
This is where it crosses from talking to doing. The agent reaches real tools and APIs, takes actions, and operates software instead of just describing what to do.
Evals and guardrails
The part everyone skips. The rule is to quantify every step, so when something breaks you can see where and why. Add the guardrails and the human-in-the-loop checkpoints, and you have a system you can trust in production instead of one you cross your fingers on.
Why it matters
The model is the easy part now. The orchestration, the memory, the tools, and the checks are where an agent works or falls apart — so “how good is the model” is the wrong question to lead with.
From the conversation
This explainer is drawn from these episodes — each carries its full transcript.
