Agentic AI vs generative AI
By Matt Doughty, CEO & Co-Founder of Prefactor ·
Generative AI produces content for a person to use; agentic AI uses a model to decide and act — calling tools, writing to systems, working a goal across multiple steps. The two get confused because every agentic system has a generative model inside it. The difference that matters is what happens to the model's output: review by a human, or execution against your systems.
| Dimension | Agentic AI | Generative AI |
|---|---|---|
| Core question | What should happen next — and do it | What content fits this prompt |
| Output | Actions: tool calls, API writes, decisions | Content: text, images, code for review |
| Shape of work | Loop — plan, act, observe, adjust, repeat | Single pass — prompt in, output out |
| Autonomy | Carries a goal across steps without re-prompting | Waits for the next human prompt |
| Failure mode | Wrong actions on real systems; errors compound | Wrong content; a human catches it before it ships |
| Oversight it needs | Identity, scoped permissions, audit trail, runtime controls | Output review and usage policy |
| Examples | Coding agent opening pull requests; support agent issuing refunds | Chat assistant drafting an email; image generation |
The verdict
Treat this as a line your systems cross rather than a technology choice: the moment model output stops being content a person reviews and becomes an action software executes, you have agentic AI — and your governance changes class with it. A generative deployment is governed like a content tool: review the output, set usage policy. An agentic deployment is governed like an employee with system access: it needs its own identity, permissions scoped to the task, and a record of what it did and why. If you are deciding what to build, build generative first where a human naturally sits between model and consequence, and go agentic only where the loop's speed is worth the controls you now owe it.