Agentic AI describes AI systems that pursue goals autonomously — planning, using tools, and adapting across multiple steps — rather than producing a single response to a single prompt.
The term marks a shift in how AI is deployed:
- Pre-agentic: AI generates text, images, or code on request. The human orchestrates every step.
- Agentic: AI receives a goal, breaks it into steps, selects tools, executes actions, and reports back — with varying degrees of human oversight.
What makes AI "agentic"
An agentic system typically combines:
- A reasoning model (LLM) that can plan and decide
- Tools that let it interact with the world (APIs, databases, browsers)
- A loop that repeats until the goal is achieved
- Memory that persists context across steps
Not every use of an LLM is agentic. A summarization prompt is not agentic. A research assistant that searches the web, reads documents, and writes a report is.
Agentic AI in business
For operators and founders, agentic AI matters because it changes what can be automated. Tasks that required human judgment at every step — triage, investigation, multi-system coordination — can now be handled by agents with appropriate architecture and guardrails.
Related reading
Start with What Is an AI Agent? for a concrete walkthrough of agentic behavior in a real workflow.