Hill-Climbing Skills: Improve Agents Without Changing the Model — Shubhankar Srivastava, Browserbase

This talk introduces a 'hill-climbing' approach to improve AI agent skills without modifying the underlying model weights. It details an 'AutoBrowse' loop where a 'doer' agent interacts with a web environment (e.g., Google Flights, conference schedules, restaurant searches), and a 'teacher' agent analyzes screenshots, traces, and results to generate 'lessons'. These lessons are stored in a reusable 'strategy file' which is then used to guide subsequent agent attempts, effectively learning from past failures and successes. The talk demonstrates how this process can discover URL shortcuts, optimize navigation, and handle site-specific constraints, leading to more efficient and reliable task completion. It also discusses the requirements for applying this method to other domains (replayable environments, verifiable outcomes) and addresses challenges like prompt injection, skill maintenance, and defining optimization objectives.