This guide breaks down 30 fundamental ideas behind AI agents—from the basic think-act-observe loop and state management to multi-agent patterns, guardrails, and observability. It shows how to configure, extend, and safely run agents in any framework by focusing on underlying principles rather than tools.

• Agents beat plain prompts/scripts only when the number of steps is unpredictable and needs feedback-driven adjustment—otherwise a script is cheaper and faster.
• Where you store state matters: code/project files go in Git, erratic or session facts go in memory, structured multi-user data goes in a database.
• Clear, targeted instructions matter more than model choice—tests show a cheaper model with specific task instructions beats a pricier model given generic prompts.
• Keep global config files (CLAUDE.md, AGENTS.md) under 100 lines and push task-specific guidance into separate workflow files loaded only when needed.