Agentic AI
AI systems designed to act, not just respond — they plan, use tools, and make decisions across multiple steps to complete a goal.
In plain English
Agentic AI refers to AI products built around an agent loop: the model decides what to do, takes an action (often via a tool), observes the result, and decides again. It's the dominant pattern of the 2025–26 AI wave — the shift from "AI that answers" to "AI that does."
What makes something "agentic":
- Goal-driven — given an outcome, not a step-by-step script
- Tool-using — calls APIs, runs code, browses the web, edits files
- Iterative — loops until the goal is met or it gives up
- Stateful — tracks progress across multiple turns
Where you'll see it: Coding agents (Cursor, Claude Code), research agents (ChatGPT Deep Research, Perplexity), customer-service agents (Sierra, Decagon), browser agents (Operator, Comet), and operations agents (Lindy, Relevance AI) are all agentic products.
The line between "AI assistant" and "agentic AI" is fuzzy. A useful test: can it complete a multi-step task without you steering each step? If yes, it's agentic.