Agentic AI is rapidly emerging as the next evolution beyond generative AI and task-based automation. This session clarifies what makes agentic systems distinct, highlighting core capabilities such as reasoning, planning, memory, and multi-step task execution, along with the importance of feedback loops and continuous interaction with the environment.

The session then outlines the architectural foundations needed to support agentic AI in the enterprise, including layered frameworks for data ingestion, model selection, orchestration, and governance. Attendees will learn how reasoning engines, knowledge graphs, and vector databases integrate to enable contextual decision-making, and how to ensure interoperability with existing systems.

Finally, the session presents a practical implementation framework—covering use-case identification, tool selection, agent design, API integration, workflow orchestration, and governance structures such as human oversight, monitoring, and compliance. Real-world examples illustrate how organizations can apply agentic AI for knowledge management and operational optimization.

LEARNING OBJECTIVES

Defining Agentic AI and Its Core Components

Architectural Foundations of Agentic AI Systems

Implementation Framework