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In this episode of the Eye on AI podcast, we dive deep into the world of AI agents with Ece Kamar, VP of Research and Managing Director of AI Frontiers Lab at Microsoft.

Ece shares her unique insights on the future of AI, discussing how AI agents are reshaping the way we interact with technology and perform tasks.

Throughout the episode, Ece explains the groundbreaking potential of AI agents, describing how they act as autonomous entities that can perceive, learn, and carry out complex tasks in real time. She discusses the revolutionary shift from traditional AI models to agentic workflows, highlighting how multi-agent systems like Microsoft's AutoGen are creating scalable solutions for industries and everyday life. Ece also shares her thoughts on building responsible AI, touching on the ethical challenges and safety concerns that come with the rise of autonomous agents.

We explore how multi-agent systems can scale to millions of agents, and how they are transforming enterprises by automating complex workflows, personalizing customer experiences, and pushing the boundaries of AI development. Ece’s perspective on the future of AI in scientific discovery, as well as her work in responsible AI, offers a thought-provoking glimpse into what lies ahead.

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(00:00) Preview and Introduction

(03:11) What Are AI Agents?

(04:43) Building Responsible AI at Microsoft

(10:55) The Rise of Agentic Workflows

(12:30) Multi-Agent Systems and AutoGen

(18:04) Scaling Multi-Agent Systems

(20:22) The Creation and Evolution of AutoGen

(23:07) Real-World Applications of AutoGen

(25:52) Large-Scale Simulations with AI Agents

(27:36) The Role of AI Agents in Scientific Discovery

(31:20) AI Agents and Complex Reasoning

(36:49) Challenges in Defining Agent Boundaries

(39:12) The Risk of Agents Interacting with Each Other

(43:59) Building Trustworthy and Safe AI Agents

(48:44) Learning from Human Factors in Automation

(50:50) Why Speed and Coordination Matter in AI Development

(55:08) The Future of AI Agents in Enterprises

(57:47) Low-Code/No-Code Development for AI Agents