Anatomy of an Agent

By the end of this lesson you will understand how an AI agent autonomously navigates and solves multi-step problems. It starts with the agentic loop: unlike a standard chatbot, an agent receives a prompt, decides to call a tool, evaluates the result, and repeats this cycle until the task is complete. Agents rely on tools (functions they can execute to act on the outside world) and memory (so they can persistently store knowledge for long-horizon tasks). Effective agents plan before acting and implement error control to recover gracefully from tool failures. Worked example: a customer-support agent. When a user asks for a refund, the agent enters its loop, it uses a tool to fetch the order history, reads its memory for prior interactions, plans how to handle the refund, executes a database tool, and finally responds. Practice task: map out a workflow for a daily task, naming three exact tools the agent would need and the facts it should store in memory over time.