To add some color to this

Anthropic does a good job of breaking down some common architecture around using these components [1] (good outline of this if you prefer video [2]).

"Agent" is definitely an overloaded term - the best framing of this I've seen is aligns more closely with the Anthropic definition. Specifically, an "agent" is a GenAI system that dynamically identifies the tasks ("steps" from the parent comment) without having to be instructed that those are the steps. There are obvious parallels to the reasoning capabilities that we've seen released in the latest cut of the foundation models.

So for example, the "Agent" would first build a plan for how to address the query, dynamically farm out the steps in that plan to other LLM calls, and then evaluate execution for correctness/success.

[1] https://www.anthropic.com/research/building-effective-agents [2] https://www.youtube.com/watch?v=pGdZ2SnrKFU

This sums up as ranging from multiple LLM calls to build a smart features to letting the LLM decide what to do next. I think you can go very far with the former but the latter is more autonompus in unconstrained environments (like chatting with a human etc.)