In depth
The defining feature of agentic AI is the loop. A non-agentic system gets one prompt, returns one answer, and stops. An agentic system runs the model in a loop where each turn the model can call a tool, read the tool's result, and decide what to do next. That loop is what turns a language model from a writing assistant into something that can complete a job. The loop runs until the model decides it is done, hits a step limit, or surfaces a decision back to a human.
The technical underpinnings come from a few well-known patterns. ReAct, published by Yao et al. in 2022, formalized the reason-then-act loop. Tool-use APIs from Anthropic, OpenAI, and Google expose function-calling primitives that let a model emit a structured request for the host program to run. Frameworks like LangGraph, CrewAI, and the Anthropic Agent SDK provide scaffolding around those primitives: state management, message history, retries, observability.
What agentic AI is not: artificial general intelligence, deterministic process automation, or a single product category. It is a posture. Running a model in a loop with tools. The same posture applies to small problems and large ones. A scheduler that picks the best 30-minute slot for a meeting can be agentic. A research workflow that reads ten papers and writes a synthesis can be agentic. Both share the same underlying pattern, even though the scope looks nothing alike.
The common confusion is between “agentic” and “autonomous.” Autonomous implies no human in the loop. Agentic only means the system runs in a loop with tools. Most production agents today have human checkpoints built in, either as approval steps before a high-stakes action or as a review stage before output ships.