Agentic AI refers to AI systems that can act autonomously as agents, setting goals, making plans, and taking actions to complete tasks with limited human input.
Unlike traditional AI that responds only to single prompts, agentic AI maintains ongoing goals and uses reasoning loops to decide what steps to take next.
It typically combines large language models with capabilities like planning, memory, tool use, and self-reflection to break down complex tasks and adapt when obstacles arise.
The system evaluates outcomes, adjusts its approach, and can chain multiple actions together until the original objective is achieved.
An agentic AI travel assistant could receive a request to plan a vacation, then independently search flights and hotels, compare prices using web tools, book the best options within a budget, and email the itinerary without further instructions.
Agentic AI moves systems from passive responders to active problem-solvers, enabling automation of multi-step workflows that were previously too complex for AI.
Regular chatbots answer one question at a time, while agentic AI can pursue a goal over many steps and use tools without needing new prompts for each action.
An AI Agent (or Agent) is a software system that perceives its environment, reasons about goals, and takes actions autonomously to complete tasks.
Reinforcement Learning (RL) is a machine learning method where an agent learns to make sequential decisions by interacting with an environment, receiving rewards or penalties, and aiming to maximize its long-term reward.
Tool Use (aka Function Calling) lets AI agents call external tools, APIs, or functions by outputting structured requests instead of just text.