AutoTailor improves web agent speed and accuracy by cutting unused tools
AutoTailor: Automatic, User-Aligned Capability Selection and Adaptation for Web Agents
Artificial IntelligenceSoftware Engineering
Summary
Web agents use tools to interact with websites automatically, but having too many or poorly chosen tools can slow them down and waste resources. The authors created AutoTailor, which automatically picks a smaller, smarter set of tools tailored to what users actually need. It filters out unnecessary functions and adjusts the tools as it learns what works best, making the agent more accurate and faster while using fewer computing resources. This method showed better performance on tasks compared to using many unrefined tools or fallback strategies alone.
What this means in practice
- •For software developers: Create more efficient web automation tools that automatically adjust their capabilities to user needs, reducing cloud compute costs and latency.
- •For customer support engineers: Implement adaptive web agents that maintain a compact set of browser interaction abilities tailored to common support tasks, improving response accuracy and speed.
Authors
Xinyun Cao, Adriana Szekeres, Fazle Elahi Faisal
Abstract
Web agents can utilize reusable tools to reduce the cost and latency of low-level browser interaction, but automatically discovered tool collections can be large, redundant, and poorly aligned with user demand. We present AutoTailor, a meta-agentic framework for constructing and maintaining a compact set of trajectory-derived Model Context Protocol (MCP) APIs. Offline, AutoTailor converts web trajectories into parameterized browser-automation programs, applies a Quality Filter to remove APIs with unsuitable granularity and redundant functionality, and applies a Usage Likelihood Filter to prioritize broadly useful capabilities while preserving semantic coverage. Online, Dynamic Reselection monitors task outcomes and API usage, identifies recurring coverage gaps, adds relevant candidates, and prunes persistently unused capabilities. We evaluate AutoTailor on 106 WebArena Postmill tasks. Offline filtering reduces the initial 1,283 unrefined APIs to 87, and Dynamic Reselection produces a 33-API set. With reasoning and acting (ReAct) fallback, this set achieves 90.6% correctness, compared with 87.5% for ReAct alone, while reducing average total request-token cost by 57.8% and latency by 29.4%. Without ReAct, it achieves 60.1% correctness, marginally matching the performance of unrefined set, while reducing request-token usage by 94.9%. Together, these results show that static filtering produces a compact inventory of APIs expected to support core, high-likelihood tasks, while dynamic reselection further tailors that inventory to observed user needs. This combination improves accuracy and latency while sharply reducing token usage and end-to-end cost, demonstrating the value of user-aligned capability management for efficient web agents.