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Beyond Browsing: API-Based Web Agents

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arxiv 2410.16464 v3 pith:VWZEQQW7 submitted 2024-10-21 cs.CL cs.MA

Yueqi Song , Frank Xu , Shuyan Zhou , Graham Neubig This is my paper · ORCID
classification cs.CLcs.MA
keywords agentsbrowsingapistasksonlineagentaloneapi-based
verification ladder T0 review T1 audit T2 compute T3 formal
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Web browsers are a portal to the internet, where much of human activity is undertaken. Thus, there has been significant research work in AI agents that interact with the internet through web browsing. However, there is also another interface designed specifically for machine interaction with online content: application programming interfaces (APIs). In this paper we ask -- what if we were to take tasks traditionally tackled by Browsing Agents, and give AI agents access to APIs? To do so, we propose two varieties of agents: (1) an API-calling agent that attempts to perform online tasks through APIs only, similar to traditional coding agents, and (2) a Hybrid Agent that can interact with online data through both web browsing and APIs. In experiments on WebArena, a widely-used and realistic benchmark for web navigation tasks, we find that API-Based Agents outperform web Browsing Agents. Hybrid Agents out-perform both others nearly uniformly across tasks, resulting in a more than 24.0% absolute improvement over web browsing alone, achieving a success rate of 38.9%, the SOTA performance among task-agnostic agents. These results strongly suggest that when APIs are available, they present an attractive alternative to relying on web browsing alone.

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Cited by 15 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    cs.AI 2026-08 conditional novelty 6.0 of 10

    An extraction-first pipeline, CatalogBrowse plus DegreeMap, reconstructs university curricula from heterogeneous public sources and certifiably optimizes feasible personalized degree plans, though no code or data is released.

  2. Experience-Evolving Multi-Turn Tool-Use Agent with Hybrid Episodic-Procedural Memory

    cs.LG 2025-12 conditional novelty 6.0 of 10

    An LLM agent that builds a tool-transition graph with state summaries from past experience improves tool selection and RL exploration by large margins on multi-turn benchmarks.

  3. Doc2Agent: Scalable Generation of Tool-Using Agents from API Documentation

    cs.CL 2025-06 reject novelty 6.0 of 10

    Doc2Agent automatically converts unstructured REST API documentation into validated, Python-based tools for AI agents, reporting a 55% relative WebArena improvement over direct API calling.

  4. Build the web for agents, not agents for the web

    cs.LG 2025-06 conditional novelty 6.0 of 10

    The paper proposes a paradigm shift: design a standardized Agentic Web Interface for AI agents, rather than adapting agents to human-facing websites.

  5. Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction

    cs.LG 2025-06 conditional novelty 6.0 of 10

    Scaling the number of interaction steps, trained via a curriculum over rollout horizon, improves web-agent task success and outperforms scaling per-step reasoning under fixed token budgets.

  6. WebChoreArena: Evaluating Web Browsing Agents on Realistic Tedious Web Tasks

    cs.CL 2025-06 conditional novelty 6.0 of 10

    WebChoreArena adds 532 human-curated, memory-intensive tasks to WebArena's reproducible environments, and current LLM agents score 2.6% to 44.9%, much lower than on WebArena.

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    cs.AI 2025-10 conditional novelty 5.0 of 10

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    WebWalkerQA measures how well LLM agents traverse websites by clicking subpages, and WebWalker, an explore-critic agent, improves accuracy when added to RAG systems, though all systems remain below 40%.

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    Adding 1 to 2 human demonstrations improves web agent task success by 3.36 to 7.21 absolute percentage points on Mind2Web and VisualWebArena, though the best adapted agents still succeed less than 25% of the time.

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