Large language models are powerful, but their true potential emerges when they evolve into AI agents which are systems that can reason, plan, and take action autonomously. My talk will explore the shift from using models as passive tools to designing agents that actively interact with data, systems, and people.

I will cover: - Gen AI and Agentic AI – How are These Different - Single Agent (monolithic) and Multi Agent Architectures (modular / distributed) - Open Source and Closed Source AI Systems - Challenges of Integrating Agents with Existing Systems

I will break down the technical building blocks of AI agents, including memory, planning loops, tool integration, and feedback mechanisms. Examples will be used to highlight how agents are being used in workflow automation, knowledge management, and decision support.

I will wrap up with where limitations of AI Agents still pose risks: - Assessing Maturity Cycle of Agents - Cybersecurity Risks of Agents

By the end, attendees will understand: - What makes AI agents different from LLMs - Technical considerations required to build AI Agents responsibly - Applicable knowledge to begin experimenting with agents.