Agentic AI is a form of artificial intelligence that enables software agents to operate with autonomy, allowing them to independently make decisions and complete tasks aligned with defined goals, often with minimal human supervision. Unlike traditional AI—which works within fixed boundaries and typically requires continuous oversight—agentic AI utilizes advanced algorithms, including large language models (LLMs), to adapt, learn from experience, and act purposefully in dynamic environments.
Core characteristics:
Autonomy: Agentic AI agents can set, pursue, and manage objectives without constant intervention, making decisions in real time.
Goal-driven behavior: Agents are designed to accomplish specific tasks, ranging from simple, repetitive actions to complex, multistep problem-solving.
Adaptability: These agents leverage techniques like reinforcement learning to evolve strategies based on feedback and changing conditions.
Orchestration: In multi-agent systems, individual agents tackle subtasks, collaborating under orchestrating frameworks that coordinate their efforts to achieve broader objectives.
Integration capabilities: Agentic AI can interact with external tools, databases, APIs, or even physical devices, making it suitable for use cases from business automation to healthcare, finance, and IoT.
Distinct from Generative AI: While generative AI (like ChatGPT) focuses on creating content (text, images, code) in response to prompts, agentic AI goes further: it uses generated outputs to perform actions—such as booking flights after researching travel options—without explicit step-by-step instructions from users.
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