Agentic AI

How it works

Agentic AI describes a design choice more than a product category. A system is more agentic the more it decides on its own: which steps to take, in which order, with which tools, and when it is finished. At one end sits a single model call that drafts an email. At the other sits a group of AI agents that hand work to each other, call APIs and change records with little review.

Most useful business systems sit in the middle. Code fixes the route, and the model handles the steps that need judgment, such as reading a messy document or picking the right reply template.

How businesses use it

The term shows up in vendor pitches far more often than in working systems. In June 2025 Gartner forecast that over 40% of agentic AI projects will be canceled by the end of 2027 (opens in a new tab), citing escalating costs, unclear business value and weak risk controls. The same release warned about "agent washing": existing assistants, chatbots and RPA products relabeled as agents.

The companies getting value pick a process with a countable output (quotes sent, tickets closed, invoices posted, leads routed), give the system limited permissions and widen them only after it has run cleanly for a few weeks. An AI readiness audit is the inexpensive way to find which of your processes justify this design and which do not.

Where it goes wrong

Autonomy adds cost before it adds value. Every step the model decides on its own needs logging and a way to undo it. Projects stall when teams start with an open-ended goal ("run our marketing") instead of a bounded task. They also stall when nobody owns evaluation, so nobody can say whether version 2 is better than version 1.

Many tasks pitched as agentic are fixed sequences. If you can draw the steps on a whiteboard and they do not change from case to case, a workflow in n8n or Make does the job for less money. In that case we build the workflow, and the model only appears where a step needs reading or judgment.