Terms like 'AutoGPT,' 'agent-based systems,' and 'agentic AI' are trending, building on the momentum of Generative AI. This approach promises systems capable of autonomously executing complex tasks while adapting to changes with minimal human intervention.

It’s one thing to promise, and another entirely to deliver. As the hype around “AI agents” grows, so does the confusion: What exactly are they? How do they differ from traditional AI models? Why do so many implementations fall short of their promises?

In this talk, we’ll go over what AI agents really are: how they work, where they succeed, and where they fail. We’ll look at some agentic AI design patterns and how they are analogous to traditional software tools, databases, networks, and peer-to-peer systems.

Learning objectives:
* What is an AI Agent?
* How do they differ from traditional AI? From generative AI (e.g. LLMs)?
* When to use / when not to use
* Design patterns
* Multi-agent