AI Agent Architecture Diagram Examples

These AI agent examples show how the same building blocks — reasoning loop, LLM, memory, and tools — are arranged for different agent patterns, from a simple tool-caller to a planning agent that breaks work into steps.

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Real examples

ReAct agent (reason + act)

Who uses it: Developer building a first tool-using agent

Loop: thought → action → observation → repeat until answer

LLM decides at each step whether to call a tool or finish

Tools: search, calculator, a single external API

Memory: a scratchpad holding the reasoning trace for this task

Stops when the LLM emits a final answer instead of an action

Why this works: ReAct is the default starting pattern — the agent interleaves reasoning and tool calls in one loop, so you can read its scratchpad to see exactly why it chose each action when something goes wrong.

Plan-and-execute agent

Who uses it: Engineer whose tasks need many steps and get off track

Planner: LLM first writes a full step-by-step plan

Executor: a separate loop runs each step, calling tools

Re-planner: revises the plan if a step fails or returns surprises

Memory: the plan itself plus results of completed steps

Cheaper model for execution, stronger model for planning

Why this works: Plan-and-execute separates 'what to do' from 'doing it' — useful when tasks are long, because the agent commits to a plan up front instead of re-deciding the whole strategy at every single step.

Memory-augmented assistant

Who uses it: Team building an agent that remembers users across sessions

Short-term: current conversation window

Long-term: vector DB of past conversations and user preferences

Before each turn, the agent retrieves relevant long-term memories

After each turn, salient facts are written back to long-term memory

Tools: the usual set plus a 'recall' tool that queries memory

Why this works: Memory augmentation is what turns a stateless chatbot into an assistant that feels like it knows you — the retrieve-before, write-after pattern keeps the prompt small while still giving the agent access to everything it has learned.

Multi-tool research agent

Who uses it: Analyst automating data gathering across sources

Tools: web search, SQL database, PDF reader, code sandbox

Agent picks the right tool per sub-question

Code sandbox lets it compute on retrieved data, not just fetch it

Guardrail: tool calls are sandboxed and rate-limited

Final step: LLM synthesizes tool outputs into a report

Why this works: A multi-tool agent is most useful when no single source has the answer — the value is in the agent choosing which tool fits each sub-question and combining the results, which is exactly what the diagram should make legible.

Tips for better study mind maps

Draw the feedback loop explicitly — agent → tool → back to agent — because the loop is what makes it an agent rather than a pipeline.
Keep the reasoning block and the LLM as separate boxes; the reasoning policy (ReAct, plan-execute) is a design choice independent of which model runs it.
Split memory into short-term and long-term visually — they have different lifetimes and storage.
Put the tool layer in its own frame so it's clear which capabilities the agent is allowed to use.

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