Thought Exercise: Designing a Self-Improving Web Agent
Explore how to design a web agent that improves itself by autonomously creating, testing, and managing reusable skills. Learn to address redundancy, create robust functions from tasks, and build a scalable skill library to enhance agent efficiency and adaptability over time.
In our previous lessons, we explored the architecture of WebVoyager, an agent designed to complete live web tasks with a higher probability of success than traditional web agents. But what happens after the task is done? How does an agent continue to get smarter over time?
One of the key limitations of many web agents is the generalization problem. While they often perform well on the specific tasks they are designed for, they struggle to adapt to new websites or apply what they’ve learned in one context to another. This is because most agents lack a built-in mechanism for self-improvement and the ability to abstract procedural knowledge that can be reused across tasks.