Agentic AI
AI systems that can take multiple steps toward a goal on their own, rather than just answering a single question.
What is Agentic AI?
Agentic AI describes systems that can pursue a goal through a sequence of steps (deciding what to do next, taking an action, checking the result, and adjusting) rather than producing one answer and stopping. For example, instead of just answering "what's a good tent under $200?", an agentic system could search several retailer sites, compare specs and reviews, narrow the options, and add the best match to a cart. The defining trait is that it acts on its own within limits a person set, rather than waiting for a new prompt at every turn.
Why it matters
Technically, agentic AI is built by pairing a large language model with tools (search, code execution, APIs) and an orchestration loop that lets the model plan, call a tool, observe the output, and re-plan, a cycle often called the "agent loop." Multi-agent setups coordinate several specialized agents (a planner, a retriever, a checkout agent) through an orchestration layer rather than one monolithic model doing everything. Enterprise vendors distinguish it from earlier "robotic process automation" by pointing to autonomy, goal-driven behavior, and adaptability to unstructured situations, since agentic systems can handle cases nobody scripted in advance. Because they can take real-world actions (spending money, sending messages), most production deployments add guardrails such as approval steps, spending limits, or human-in-the-loop checkpoints.