What Actually Makes an AI Agent Work: Planning, Memory, and Verification

AI agents are not magic — they are software workflows where a language model selects the next step and calls external tools, with their reliability depending entirely on the orchestration around the model. Every functional agent relies on five core components: planning, tool use, memory, constraints, and verification. A key insight is that models select tools based on their descriptions rather than their names, making precise, detailed tool documentation critical to avoiding incorrect or hallucinated tool calls. Strict input validation using JSON schemas helps catch and correct bad tool calls mid-loop before they cause downstream failures. Understanding these mechanics is essential for developers building agents that interact with real systems like databases or file systems.