What Actually Makes an AI Agent Work: The Five Parts and the Framework Behind Them

An AI agent is not simply a smarter language model but a system built around one, combining tools, memory, and a coordinating framework to bridge the gap between reasoning and real-world action. The language model at the core — such as GPT, Claude, or Gemini — can only produce text, meaning it decides what to do next but cannot execute any action itself. Tools like web search, code execution, or database queries extend the model's reach, but it is the framework, such as LangChain, that actually runs those tools and returns results to the model. Because language models retain no memory between turns, the framework also maintains a running transcript of the task, re-injecting it each turn so the agent can build on previous steps. Together, these components transform a standalone model into a system capable of carrying out multi-step tasks in the real world.