Most developers think AI agents work like this: prompt → response In reality, production agents look more like this: prompt → planning → tool execution → evaluation → loop Understanding this lifecycle is the difference between a demo and a real system. Step 1: The prompt (intent layer) Prompts define the goal, not the execution. Challenges: no strict schema hard to test sensitive to wording In practice, prompts behave like an unstable logic layer. Step 2: Planning (reasoning layer) The agent interprets the prompt and creates a plan. Typical patterns: ReAct Chain-of-thought task decomposition This is where decisions happen. Step 3: Tool execution (action layer) This is where things get real. The agent: calls APIs writes data triggers workflows Without constraints, this becomes dangerous. Best practices: validate inputs restrict permissions log every action Step 4: Evaluation (control layer) After each action, the agent evaluates: Did it succeed? Should it retry? Should it change strategy?…