AI Agent Architecture Diagram
Visualize the reasoning loop, tools, and memory that let an AI agent plan and act.
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7 connected components you can rename, recolor, and extend with AI.
An AI agent architecture diagram illustrates how an autonomous agent perceives a goal, reasons, and acts. It centers on an LLM-powered reasoning core that orchestrates a plan-act-observe loop, calling external tools and APIs, reading and writing memory, and iterating until the task is complete.
Developers building agentic applications, automation engineers, and AI product teams use this agent diagram to design assistants that book meetings, run code, or query systems autonomously. It clarifies how planning, tool use, and short- and long-term memory fit together when explaining AI agent architecture to teammates or investors.
Great for
- Agentic app design
- Automation workflow planning
- AI product roadmaps
- Investor pitch decks
- Engineering onboarding docs
Frequently asked questions
What is AI agent architecture?+
It is the design that lets an LLM act autonomously by combining a reasoning core, planning, tool calling, and memory in a loop that observes results and decides the next action toward a goal.
What are the components of an AI agent?+
Key components are a reasoning or planning core, a set of callable tools and APIs, short-term working memory, long-term persistent memory, and an executor that performs actions and feeds results back.
How does an AI agent use tools?+
The agent's reasoning core decides which tool to invoke, formats a structured call, executes it against an API or function, then incorporates the returned result into its next reasoning step.
What is the difference between an AI agent and a chatbot?+
A chatbot responds turn by turn, while an AI agent plans multi-step tasks, calls tools, and loops autonomously until a goal is achieved with minimal human input.
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