An AI agent perceives a goal, takes actions to achieve it, observes the results, and loops — planning and acting — until done. Unlike a chatbot that responds to one message, an agent operates autonomously over multiple steps, using tools and adapting its approach based on feedback from the environment.
A goal — A task that takes multiple steps to complete, not a single question to answer.
Actions — The ability to call tools: search the web, run code, read files, call APIs, send messages.
Observations — The ability to receive and process the results of those actions.
A reasoning loop — The ability to decide what to do next based on observations. Agents determine the sequence of steps dynamically — pipelines execute a fixed sequence defined in advance.
The LLM reads the current state — goal, history of actions, and observations — and decides what to do next. The agent framework executes the requested tool call, captures the result, and feeds it back as a new observation. This loop is what makes a system agentic.
Agents are not more intelligent than the underlying LLM. They are the same LLM applied iteratively, with access to tools and the ability to act. Their power comes from iteration and tool access — not a fundamentally different kind of reasoning.
Executes predefined rules: if X then Y. Fast, reliable, and predictable for known inputs. Fails on inputs outside the defined rules.
Handles tasks too varied or ambiguous for fixed rules. Can adapt to unexpected outputs, recover from intermediate failures, and handle novel inputs — because it reasons rather than rule-follows.
Ask the AI assistant about what AI agents are, how they differ from chatbots, or what makes the agentic loop work.