AI agents explained: how they actually work

The Agent Loop: Perceive → Think → Act → Observe → Repeat

Agents operate in a continuous feedback loop:

• Perceive: Ingest input (e.g., code, user query).
• Think: Reason using LLM (e.g., 'Call a calculator?').
• Act: Use tools (e.g., execute code, fetch data).
• Observe: Check results (e.g., 'Did it run?').
• Repeat: Adjust strategy and loop.

Core Components

• LLM (Brain): Reasoning engine (e.g., GPT-4, Claude 3).
• Tools (Hands): Function calling, APIs, and third-party integrations.
• Memory (Context): Conversation history, RAG, and long-term storage.
• Planning (Strategy): ReAct, chain-of-thought, or tree-of-thought.

Real Example: Claude Code Solves GitHub Issue

Claude Code uses its 200K context window to read a GitHub issue description, generate a fix, run tests, and propose a pull request—all within the agent loop.