Internal APIs Are All You Need: Shadow APIs, Shared Discovery, and the Case Against Browser-First Agent Architectures
On the map: LLM agents and tool use → Web, search, and research agents
Abstract (arXiv)
Autonomous agents increasingly interact with the web, yet most websites remain designed for human browsers -- a fundamental mismatch that the emerging ``Agentic Web'' must resolve. Agents must repeatedly browse pages, inspect DOMs, and reverse-engineer callable routes -- a process that is slow, brittle, and redundantly repeated across agents. We observe that every modern website already exposes internal APIs (sometimes called shadow APIs) behind its user interface -- first-party endpoints that power the site's own functionality. We present Unbrowse, a shared route graph that transforms browser-based route discovery into a collectively maintained index of these callable first-party interfaces. The system passively learns routes from real browsing traffic and serves cached routes via direct API calls. In a single-host live-web benchmark of equivalent information-retrieval tasks across 94 domains, fully warmed cached execution averaged 950,ms versus 3,404,ms for Playwright browser automation (3.6× mean speedup, 5.4× median), with well-cached routes completing in under 100,ms. A three-path execution model -- local cache, shared graph, or browser fallback -- ensures the system is voluntary and self-correcting. A three-tier micropayment model via the x402 protocol charges per-query search fees for graph lookups (Tier~3), a one-time install fee for discovery documentation (Tier~1), and optional per-execution fees for site owners who opt in (Tier~2). All tiers are grounded in a necessary condition for rational adoption: an agent uses the shared graph only when the total fee is lower than the expected cost of browser rediscovery.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 17 of its 34 references that are in the map. First posted on arXiv 1 Apr 2026.
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17 of 34 references.
Closest ancestors · 7
Same direction, newest first
- 2025BrowserAgent: Building Web Agents with Human-Inspired Web Browsing Actions12 Oct
- 2025Agentic Web: Weaving the Next Web with AI Agents28 Jul
- 2025Build the web for agents, not agents for the web12 Jun
- 2024Beyond Browsing: API-Based Web Agents21 Oct
- 2024WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models25 Jan
- 2023WebArena: A Realistic Web Environment for Building Autonomous Agents25 Jul
- 2023Mind2Web: Towards a Generalist Agent for the Web9 Jun
Other references · 10
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
- 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,880
- 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
- 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,302
- 2025Multi-Agent Collaboration Mechanisms: A Survey of LLMscited by 424
- 2024AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Callscited by 121
- 2024Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agentscited by 56
- 2025BetaWeb: Towards a Blockchain-enabled Trustworthy Agentic Webcited by 7
- 2025Evolution of AI Agent Registry Solutions: Centralized, Enterprise, and Distributed Approachescited by 4
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