Thought Exercise: Design a Self-Improving Web Agent
Explore how to design a web agent that continuously improves by autonomously creating reusable skills from repeated tasks. Understand the challenges of redundancy, skill synthesis, and management to build a self-improving agent that adapts and optimizes performance on real-world websites.
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?
A primary limitation of many web agents is limited generalization. They perform well on predefined tasks but struggle to adapt to new websites or transfer knowledge across contexts. Most agents lack mechanisms for self-improvement and for abstracting reusable procedural knowledge.
The goal: An agent that learns skills
Addressing this requires an agent that not only executes tasks but also learns from experience. The goal is to design a framework in which an agent can explore a website, identify common routines such ...