customer support engineers

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.

Abstract: Retrieval-Augmented Generation (RAG) for code generation has been studied extensively on public repositories, where a model's parametric knowledge often compensates for imperfect retrieval. This breaks down in enterprise codebases, where private APIs, internal frameworks, and undocumented team conventions fall entirely outside any model's pretraining distribution. Recent work on private-library code generation shows that even oracle (perfect) retrieval does not eliminate errors, but locates failures downstream in API usage; separately, confidence-gated retrieval has been studied for open-domain question answering using model-internal confidence. Neither addresses whether retrieval itself was structurally sufficient for a private-code query before generation begins. We introduce RCL (Retrieval-Confidence Layer), a lightweight module inserted between retrieval and generation that combines a call-graph-derived structural coverage score with a novelty score estimating a query's dependence on knowledge outside the model's prior, to detect insufficient retrieval before generation occurs. When confidence falls below a calibrated threshold, RCL triggers a targeted follow-up retrieval or labels the output for human review, rather than generating silently against incomplete context. We describe RCL's architecture, formalize its scoring functions, and propose an evaluation methodology using a private-code benchmark built by injecting synthetic internal APIs into open-source Java repositories, simulating the enterprise condition without proprietary code. We report results (Section 7) comparing RCL against similarity-only retrieval on generation correctness. Our position is that retrieval sufficiency, assessed structurally rather than from model-internal confidence, is a distinct and currently underaddressed signal for building safer code-generation systems in private, enterprise settings.