AI Agent Architecture Diagram Template

What this template is for

An AI agent architecture diagram shows how an autonomous LLM-powered agent perceives a request, reasons about what to do, calls tools, and uses memory to act over multiple steps. This template lays out the core of a single agent: the reasoning/planning loop (the ReAct observe-think-act cycle), the LLM that powers it, a memory layer split into short-term context and long-term vector storage, and a tool layer the agent can invoke — web search, code execution, external APIs. Use it to design a new agent, document how an existing agent decides and acts, or explain the difference between a plain LLM call and a true agent loop.

When to use this template

• Design a new autonomous agent before writing the orchestration code.
• Explain to a stakeholder why an agent is more than a single LLM API call.
• Document the tool set an agent is allowed to call for a security review.
• Show where short-term context ends and long-term memory begins.
• Trace a failure: did the agent reason wrong, call the wrong tool, or lose context?
• Compare a ReAct agent against a plan-and-execute agent by editing the loop.

How to use it

1. 1Start with the user request entering the agent at the top.
2. 2Draw the agent core: a reasoning/planning block next to the LLM that powers it.
3. 3Add the memory layer beside the agent — short-term context plus long-term vector storage.
4. 4Add the tool layer below the agent: web search, code execution, external APIs.
5. 5Connect agent → tools (the action) and tools → agent (the observation feedback loop).
6. 6Label the loop as observe → think → act to make the agentic cycle explicit.

Quick example