What makes an AI agent different from an LLM?
An LLM answers; an agent does. The difference is four things built around the model: multiple models orchestrated together, memory and context, tools it can call, and a layer of checks running the whole time. The model is just one part of the system.
1. 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 is good at. The job stops being “prompt the model” and becomes “coordinate the system.”
2. 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.
3. 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.
4. Evals and guardrails
The part everyone skips. Aishwarya Srinivasan’s 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
When someone says “we built an agent,” the real question is not how good the model is. It is what system they built around it. The model is the easy part now. The orchestration, the memory, the tools, and the checks are where it works or falls apart.
From the conversation
This explainer is drawn from these episodes — each carries its full transcript.
