This episode explores the evolving landscape of AI agents, focusing on their definition, capabilities, and future implications. Against the backdrop of the recent AI Engineer Conference, the discussion centers on Dharmesh Shah's pragmatic definition of an AI agent as "AI-powered software that accomplishes a goal," sparking a debate on the breadth versus precision of such a definition. More significantly, the conversation delves into the concept of multi-agent networks and the role of Model Callers (MCPs) as a crucial standard for interoperability and collaboration among agents. For instance, Dharmesh Shah illustrates how Agent.AI, his platform for building and deploying AI agents, leverages MCPs to create a marketplace and professional network for agents, fostering collaboration and efficient task delegation. As the discussion pivots to business models, the distinction between "Work as a Service" and "Results as a Service" is examined, highlighting the challenges and opportunities in pricing and evaluating AI-driven outcomes. Ultimately, the episode underscores the transformative potential of AI agents in reshaping workflows, teams, and business models, emphasizing the need for robust evaluation methods and a focus on solving real-world problems.
