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Agents Everywhere
Originally aired:
About the Session
This session explores recent developments in autonomous agents, focusing on their integration with Large Language Models (LLMs). We’ll dive into the emerging field of "Model-based Agents" and discuss how this approach is reshaping AI research and applications.
Agentic frameworks now enable agents to store experiences, synthesize memories over time, and dynamically retrieve them to inform behavior and decision-making. Coupled with flexible tools, these agents can handle complex multi-step tasks that once required intricate rule-based systems.
Join this session to learn what we can achieve with agents today and how their capabilities will evolve in the future.
Target Audience: This session is primarily aimed at DeepTech Professionals and AI Researchers, as it delves into cutting-edge advancements in autonomous agents and their integration with LLMs. Software Architects are the secondary audience, as they will need to understand how to design systems that incorporate agents capable of sophisticated multi-step tasks. Back-end Developers involved in AI and automation projects may also find value in learning how agentic frameworks can transform task management and decision-making processes.
Agents Everywhere
Originally aired:
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