Okay, so hear me out… I just got back from the Agentic AI Summit 2025 at UC Berkeley, and my brain is still buzzing. As someone knee-deep in a PhD focusing on AI, this was basically Christmas morning for a tech nerd like me.
It wasn’t all theoretical discussions, though. A big chunk of the summit was about practical applications, which is where things get really interesting. We’re talking about AI agents that can actually do stuff, not just churn out text. Think of them as little digital assistants that can handle complex tasks, coordinate with each other, and even use tools to get the job done.
One of the hot topics was memory. How do these agents remember what they’ve done or learned over time? It’s a huge challenge, but seeing the progress made was seriously cool. Another big hurdle is tool selection – how does an AI agent know which tool to use from a vast library to solve a specific problem? It sounds simple, but the underlying logic is incredibly complex.
We dove deep into frameworks like CrewAI and LangGraph. If you’re not familiar, CrewAI lets you build multi-agent systems where different AI agents with specific roles collaborate. LangGraph, on the other hand, is all about building stateful, multi-agent applications. Basically, they’re giving developers the building blocks to create these sophisticated AI systems.
Let’s be real, there’s a lot of hype around AI right now. The summit offered a good dose of reality checks. While the potential is massive, we’re still grappling with fundamental issues. It’s not quite the sci-fi movie scenario yet, but we’re definitely moving in that direction.
Multi-modal capabilities – AI that can understand and generate not just text, but also images, audio, and video – were everywhere. Imagine an AI agent that can analyze a video and then write a script based on it. That’s not far off.
And then there’s the economic side of things. Building and running these advanced AI agents requires significant computational resources, which translates to costs. The economics of AI development are a massive factor in how quickly we see these technologies deployed.
Overall, it was an eye-opening experience. It confirmed that AI agents are more than just a buzzword; they represent a tangible step towards more capable and autonomous AI systems. The challenges are real, but the innovation happening in frameworks and practical applications is genuinely exciting. I’m stoked to see what comes next, and you can bet I’ll be experimenting with CrewAI and LangGraph myself soon!