Agent Architecture Overview: 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.

Prompt your AI

Use these three in order. Each builds on the one before.

1. Basics & terminology

In one paragraph, explain the three core components of an AI agent (LLM, tools, memory) like I have never built one before.

2. Why it works (the mechanism)

Walk me through how a ReAct-style agent loop actually works step by step, from receiving a user query to returning a final answer.

3. Advanced — application & what's next

Given an agent tasked with researching a SaaS product's pricing, how would the interaction between its tool calls and memory layers affect the accuracy and freshness of what it reports?

References

• https://arxiv.org/abs/2210.03629