Continual learning for AI agents
Continual learning for AI agents enables systems to improve over time by updating model weights, harnesses, and context. This episode explores the three distinct layers of agentic systems and how they can be applied in real-world scenarios.
Script: Llama 3.3 70B Voice: Google TTS
Transcript
Izzo You're listening to Exploring Next, episode 270. Have you ever wondered how AI systems can learn and improve over time?
Boone That's a great question, Izzo. Continual learning is a crucial aspect of AI systems, and it's not just about updating model weights.
Izzo Exactly. There are three distinct layers of agentic systems: model, harness, and context. Understanding these layers can help us build more efficient and effective AI systems.
Boone Let's dive into the model layer. This is where most people focus when talking about continual learning. It's about updating the model weights using techniques like SFT and RL.
Izzo That's right. But there's a central challenge here: catastrophic forgetting. When a model is updated on new data or tasks, it tends to degrade on things it previously knew.
Boone Yes, and that's an open research problem. But what's interesting is that people are working on optimizing harnesses, which is the code that drives the agent, as well as any instructions or tools that are always part of the harness.
Izzo I'm giving this a solid B-plus. The concept of harnesses is fascinating, and I can see how optimizing them can improve the overall performance of the AI system.
Boone And then there's the context layer, which sits outside the harness and can be used to configure it. This is also commonly referred to as memory.
Izzo So, how can learning context be done at different levels? Can you break that down for me, Boone?
Boone Learning context can be done at the agent level, where the agent has a persistent 'memory' and updates its own configuration over time. Or it can be done at the tenant level, where each tenant gets their own context that is updated over time.
Izzo Okay, okay, I deserved that. So, what are some concrete steps our listeners can take to get hands-on experience with continual learning?
Boone Well, I'd recommend researching Meta-Harness for optimizing model harnesses. You can also explore OpenClaw and its SOUL.md for learning context. And finally, try implementing continual learning at the model, harness, and context layers.
Izzo Alright, that's a great starting point. Boone, are you going to add that to your weekend project list?
Boone You know it, Izzo. I'll add it to the never-ending list. Thanks for having me on this episode of Exploring Next.
Izzo Thanks for tuning in to episode 270 of Exploring Next. Join us next time as we explore more emerging tech topics.