Agentic AI vs Generative AI

Generative AI produces content — text, images, code — in response to a prompt. Agentic AI uses models to pursue goals in the world: planning, calling tools, taking actions and reacting to results, often over many steps. Generation is a capability an agent uses; agency is what turns a model that writes an answer into a system that gets something done, and it is the shift that creates the need for identity, authority and accountability.

Generative

Produces content from a prompt

Agentic

Pursues goals: plan, act, react

Relationship

Agents use generation as a tool

New need

Identity, authority, accountability

From answering to acting

A generative model answers; an agentic system acts — and action in the world with others is what raises the institutional questions this estate addresses. The capability (generation) is necessary but not sufficient: an agent that can act needs to be identifiable, authorised and auditable in ways a model that only writes text does not.

Questions

Is agentic AI just generative AI in a loop?

A loop is part of it, but agency adds goals, tool use, and action in the world — and with action comes the need for authority and accountability that pure generation does not have.

Which matters for the agentic internet?

Agentic AI. The agentic internet is infrastructure for systems that act, not just generate — which is why its hard problems are identity, authority and proof.