Agentic AI

Agentic AI is a way of building systems in which the model doesn't stop at a single answer; instead it is given a goal and drives it through to completion on its own: it breaks the task into steps, calls tools, checks results, and corrects its approach when something fails. The first pillar of this paradigm is agentic planning, and the second is tool use — the ability to reach for data and to act beyond text alone.

It's worth separating the paradigm from its implementation. An AI agent is a concrete, individual system that operates in this spirit — agentic AI is the broader category describing a whole class of such solutions, including multi-agent setups and agentic workflows. When a task calls for several specialized agents, agent orchestration comes into play.

In a company deployment, the agentic approach proves itself where a process has many steps and a variable course — for example, handling a ticket from classification, through checking data across systems, to drafting a reply. What matters most are clear boundaries: which tools the agent may call, at what point human approval is required, and when the loop should stop.