Agentic AI

Agentic AI is a specialized branch of artificial intelligence dedicated to creating AI agents that can reason, plan, and act independently in complex settings. These agents go beyond reaction to input- they actively establish goals, evaluate alternatives, and modify their actions to achieve specific results. For example, an AI agent trained to be a logistics manager can optimize delivery routes based on historical data and inventory availability, adjusting plans to reduce delays and costs. The training process often employs reinforcement learning, enabling AI agents to gain insights through interactions and feedback in simulated and real-world scenarios, alongside methods like planning algorithms and knowledge representation to facilitate intricate decision-making.

Agentic AI distinguishes itself from other types of AI by prioritizing goal-oriented behavior. In contrast to conventional machine learning models that primarily focus on recognizing patterns or making predictions, agentic AI equips AI agents with the ability to make independent decisions and take actions. It is distinct from simpler AI systems, such as chatbots, due to its capability for reasoning and planning rather than merely responding to user inputs. The fundamental distinction lies in the AI agent’s ability to solve problems and actively pursue goals in a dynamic and often unpredictable environments.