Agentic AI refers to AI systems that can pursue objectives by perceiving context, making decisions, planning steps, and taking actions with some degree of autonomy. Such systems often combine models, tools, memory, reasoning, and feedback loops, and may operate independently or cooperatively with other agents under human-defined goals and constraints.
A system or program that can autonomously perform tasks on behalf of a user or another system by designing its workflow and using available tools. The system has 'agency' (hence the name) to make decisions, take actions, solve complex problems, and interact with external environments beyond the data upon which the system's machine learning (ML) models were trained.
refers to AI systems composed of agents that can behave and interact autonomously in order to achieve their objectives. These agents are small, specialised pieces of software that can make decisions and operate cooperatively or independently to achieve system objectives. Advances in agentic AI are driven by the integration of large language models (LLMs) with agent-based systems. By providing reasoning and discovery abilities, LLMs enhance an agent's autonomy. This enables the agent to determine the most appropriate course of action to meet system objectives.
A side-by-side comparison of Agentic AI and AI Agent. Understand how agentic AI describes a class of autonomous goal-pursuing systems, while an AI agent is the entity or software system that acts.
A side-by-side comparison of Agentic AI and Autonomous Systems. It explains how AI systems that plan and act toward goals differ from the broader category of systems that operate without continuous human intervention.