AI Concepts · Side by side
Agentic AI vs Generative AI
Generative AI produces content, text, code, images, in response to a prompt. Agentic AI uses those models as a brain to autonomously plan, call tools, and take multi-step actions toward a goal. Agentic systems are built on top of generative models; the difference is autonomy and action.
Agentic AI
AI that plans and acts toward goals
Generative AI
AI that produces content from prompts
Side by side
| Agentic AI | Generative AI | |
|---|---|---|
| Core job | Plan and act to achieve a goal | Generate an output from a prompt |
| Autonomy | Multi-step, self-directed | Single-turn, you drive each step |
| Tools | Calls APIs, runs code, uses MCP servers | Typically none, pure generation |
| Example | An agent that books travel end-to-end | A model that writes the email |
| State | Maintains context/memory across steps | Mostly stateless per request |
The Verdict
Use generative AI when you need content on demand. Use agentic AI when you need a task completed autonomously, research, multi-file code changes, workflows. In practice, agentic systems wrap generative models with planning, memory, and tools.
Want an agentic workflow without the setup?
Spin up an open-source AI coding agent with Creative Tim, bring your own key or run it on credits.
Frequently asked questions
Is agentic AI just generative AI with tools?
Largely yes, an agent adds planning, memory, and tool-use (e.g. MCP servers) on top of a generative model so it can act, not just generate.
Which one powers coding agents?
Coding agents like Claude Code are agentic: they use a generative model to plan and then edit files and run commands autonomously.
Keep comparing