Enterprise AI Agent Architecture

This single diagram captures what an enterprise AI agent actually consists of — and covers roughly 40% of everything you need to know. It shows the core parts working together.

How it works

1. 1A user request comes in via an API / FastAPI layer to the agent runtime.
2. 2A planner handles goal understanding, task decomposition and reasoning.
3. 3Memory (short-term, long-term, conversation, user context) is backed by a vector DB for RAG.
4. 4A tool router selects and orchestrates tools (MCP servers, GitHub, Slack, SQL/DB, APIs).
5. 5The LLM reasons, generates and calls functions, returning the final response.

Key points

• Covers AI agent, LLM, memory, tool calling, RAG, APIs, MCP and enterprise architecture.
• The planner, memory and tool router are the three core pillars.
• The vector DB powers semantic search and RAG storage.
• One diagram that captures most of what an enterprise agent needs.

Why it matters

A compact mental model of an agent's core parts — planner, memory, tools, LLM — makes everything else easier to learn and design.

Frequently asked questions

What are an agent's core parts?

A planner, memory (with a vector DB for RAG), a tool router, and the LLM that reasons and generates.

Where does RAG fit?

In the memory layer — a vector DB stores embeddings for semantic retrieval.