AI agents differ from chatbots by operating in a continuous loop: they use tools, observe results, and decide next steps until a task completes. The ReAct pattern (Reason + Act) powers agents like Claude Code and Cursor, enabling them to autonomously read files, execute commands, and adapt based on feedback. Building a basic agent requires a while loop that calls an LLM, executes tool requests, and feeds results back. Common failure modes include infinite loops, wrong tool selection, context loss in long tasks, and compounding errors. Agents work best with clear, verifiable tasks rather than subjective goals.
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