Agentic AI
AI systems that plan and take multi-step actions toward a goal, using tools and feedback, rather than producing a single response.
Agentic AI describes AI systems that exhibit agency: they break a goal into steps, choose actions, use external tools, and adjust based on the results of those actions. The defining contrast is with a single-shot model that produces one output from one prompt. An agentic system runs over multiple turns, maintains context about progress, and can recover or replan when a step fails. This makes it suited to tasks that require sequencing work across several tools or data sources.
The capability comes with added operational concerns, because a system that can take actions can also take wrong ones. Practitioners typically constrain agentic behavior with scoped permissions, guardrails, logging of every action, and approval gates for steps that change data or spend money. In commercial AI-workforce products, agentic behavior is what lets agents do real work in a customer's systems, while human-in-the-loop review keeps the autonomy bounded.
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