What's Inside an AI Agent (LLM + Tools + Memory)

Why this matters

Modern AI agents are not just chatbots — they are reasoning systems that combine a language model with external tools and persistent memory to act autonomously. Understanding this architecture is the foundation for knowing how agents find, evaluate, and remember your product.

Try it yourself

1. Draw the loop: identify which step in a ReAct-style agent loop corresponds to 'observe', 'think', and 'act' — label each with a concrete example involving product research.
2. Find a published agent framework (LangChain, AutoGen, or CrewAI) and locate where tool registration happens in the source or docs — note the function signature used to attach a tool.
3. Swap the memory store in a mental model: predict how agent output would change if episodic memory were disabled versus semantic memory — write one sentence for each.

References

• https://arxiv.org/abs/2210.03629