AgentOccam offers a simple but strong baseline for LLM-based web agents. By providing a URL and the task you want it to perform, AgentOccam can execute it for you. Its simplicity and effectiveness allow you to run it directly, or adapt it into a larger pipeline for its executing web tasks, such as web information retrieval before processing the documents.
Without using in-context examples, new agent roles, online feedback, or search strategies, AgentOccam demonstrates impressive performance on tasks in WebArena (a web simulator benchmark with tasks from sites like shopping, shopping admin, GitLab, Reddit, map, etc.) and tasks with golden answers in WebVoyager (a benchmark based on real web tasks), once surpassing the SOTA on both leaderboards.
In brief, our approach aligns the input (webpage descriptions, i.e., agent observations) and output (action strings that can be translated into web interactions, i.e., agent actions) of web tasks, with the tasks that LLMs are most familiar with, such as reading comprehension and question-answering. We refer to our approach agent observation and action space alignment, shedding light on LLMs' impressive zero-shot performance on web tasks, and the critical role of carefully tuning observation and action spaces for LLM-based agents.
