How to Build an AI Agent That Works in Loops

An AI agent is designed to work continuously towards a specific goal, repeating tasks and refining its actions based on feedback, unlike a chatbot that provides a single answer. Building such an agent involves a trigger to start, a clear goal, tools for execution, state management, a checking mechanism, and a stop condition. This looping process, where each action generates evidence influencing the next, is key to an agent’s effectiveness.

• AI agents are designed to work continuously towards a goal, repeating tasks until completion.
• Key components of a working AI agent loop include: Trigger, Goal, Tools, State, Check, and Stop.
• The agent’s effectiveness comes from its ability to observe, act, use tools, inspect results, update state, and check completion in a repeating cycle.
• Newer SDKs like Claude Code and OpenAI’s Agents SDK simplify agent creation by managing loops, tools, and sessions.
• The core principle is to start with a narrow job, make verification visible, and avoid unnecessary complexity in memory, tools, or additional agents.
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