Thought Exercise: Designing a Self-Improving Web Agent
Explore how to design a self-improving web agent that learns from experience by creating reusable functions for recurring web tasks. Learn to architect mechanisms for skill acquisition, testing, and management to make web agents faster, cost-effective, and more reliable over repeated tasks.
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.
The goal: An agent that learns skills
To overcome this, we need to design an agent that doesn’t just execute tasks but also learns from its experience. The goal of this exercise is to architect a framework where an agent can explore a website, identify common routines (like searching for a product or filling out a form), and then turn those routines into a reusable library of skills.
In this interactive exercise, you will step into the role of an agentic system designer. We will guide you, step-by-step, through the ...