AI Agent Architecture — How Autonomous AI Works
AI agents combine LLMs with tools, memory, and planning loops to complete multi-step tasks autonomously. Understanding the architecture helps you choose the right agentic product.
TL;DR: AI agents combine LLMs with tools, memory, and planning loops to complete multi-step tasks autonomously. Understanding the architecture helps you choose the right agentic product.
The 4 Core Components
Every AI agent has: (1) LLM Brain — the reasoning engine, (2) Tools — functions the agent can call (web search, code execution, file access), (3) Memory — short-term context + optional long-term vector store, (4) Planning — breaking goals into subtasks.
The ReAct Loop (Think → Act → Observe)
Most agents use the ReAct pattern: Reason about the next step → Take an action (call a tool) → Observe the result → Reason again. This loop repeats until the task is complete or the agent gives up.
Single-Agent vs Multi-Agent
Single agents handle tasks alone. Multi-agent systems (like Claude Code) use an orchestrator that delegates to specialized sub-agents — one for planning, one for execution, one for review. This improves parallelism and quality on complex tasks.
Real Products Built on Agent Architecture
Cursor and GitHub Copilot are coding agents. Perplexity is a research agent. Claude Code and OpenAI Codex are full agentic coding environments. Devin (Cognition) is an autonomous software engineer agent.