AutoTailor: Automatic, User-Aligned Capability Selection and Adaptation for Web Agents
Cao (University of Michigan) with Szekeres and Faisal (Microsoft Research Redmond) present AutoTailor, a meta-agent that turns web trajectories into MCP browser-automation APIs and then keeps that API set small and matched to what users actually ask for.
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Offline filtering. Trajectories become parameterized browser programs; a Quality Filter removes APIs of the wrong granularity or with duplicate function, and a Usage Likelihood Filter keeps broadly useful ones, cutting 1,283 candidate APIs to 87.
Online reselection. Dynamic Reselection watches task outcomes and API usage, adds candidates for recurring gaps and prunes unused ones, ending at a 33-API set.
With ReAct fallback. On 106 WebArena Postmill tasks the 33-API set reaches 90.6% correctness against 87.5% for ReAct alone, with 57.8% lower request-token cost and 29.4% lower latency.
Without fallback. API-only execution reaches 60.1%, roughly matching the unrefined API set, while using 94.9% fewer request tokens.
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.