Context Engineering Over Prompt Engineering: How AI Agents Actually Work

AI agents differ from standard chat models by autonomously planning and executing tasks toward a set goal, rather than simply answering questions. Every agent operates on a core loop of observing context, thinking through the next action, and acting — repeating until the task is complete. An agent's effectiveness depends on four components: a large language model, the loop itself, tools for external integrations, and structured context files. The Model Context Protocol (MCP) allows agents to connect seamlessly with services like Gmail, Notion, and Stripe without custom integration work for each tool. The central argument is that investing in rich, persistent context — through files like agents.md and memory.md — reduces the need for complex prompts and delivers more consistent results over time.