Agents have moved from demos to daily work faster than almost anyone planned for. In our State of Agentic AI report, 60% of organizations already run AI agents in production, and yet 40% name security and compliance as the number-one thing holding them back from scaling further. That gap, between what teams have already shipped and what they can safely operate, is the real story of AI agents right now.
But what is an AI agent, and why does the term suddenly stretch from a coding assistant to an autonomous research system? The short version is that an agent doesn’t just respond, it acts: give it a goal and it’ll plan the steps, call tools, check the results, and adjust, usually without stopping to ask. That’s what separates an agent from the generative AI it’s built on, and it’s why where an agent runs matters as much as which model sits behind it.
Key takeaways
• An AI agent pursues a goal on its own. It reasons, picks tools, and takes actions in a loop rather than answering one prompt at a time.
• The model decides, tools act, and the environment is where those actions land.
