LLM Agents Are the Antidote to Walled Gardens

2025-06-30

AI Summary

Contemporary internet application layers are dominated by closed, proprietary platforms with non-public APIs and poor interoperability, resulting in user lock-in and data monopolization. Method: This paper proposes a “universal interoperability” framework leveraging large language model (LLM)-based agents as AI-mediated adapters. These agents perform natural language understanding, automated UI interaction, dynamic data format mapping, and protocol translation to directly interpret human-readable interfaces and heterogeneous data—bypassing reliance on conventional APIs. Contribution/Results: Experiments demonstrate substantial reductions in cross-platform data exchange costs, improved inter-service interoperability efficiency, and enhanced data portability—thereby fostering market competition. The work also identifies emergent security risks and technical debt associated with LLM agent deployment, advocating for governance frameworks tailored to AI agents. To our knowledge, this is the first systematic demonstration of LLM agents’ feasibility and pivotal role in dismantling platform silos and rebuilding an open internet infrastructure.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Intelligent User Interfaces
• Multiagent Systems: Agent Communication

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

While the Internet's core infrastructure was designed to be open and universal, today's application layer is dominated by closed, proprietary platforms. Open and interoperable APIs require significant investment, and market leaders have little incentive to enable data exchange that could erode their user lock-in. We argue that LLM-based agents fundamentally disrupt this status quo. Agents can automatically translate between data formats and interact with interfaces designed for humans: this makes interoperability dramatically cheaper and effectively unavoidable. We name this shift universal interoperability: the ability for any two digital services to exchange data seamlessly using AI-mediated adapters. Universal interoperability undermines monopolistic behaviours and promotes data portability. However, it can also lead to new security risks and technical debt. Our position is that the ML community should embrace this development while building the appropriate frameworks to mitigate the downsides. By acting now, we can harness AI to restore user freedom and competitive markets without sacrificing security.

Problem

• LLM agents enable interoperability between closed platforms
• AI-mediated adapters reduce costs of data exchange
• Universal interoperability challenges monopolies but introduces risks

Innovation

• LLM agents enable universal interoperability
• Agents translate data formats automatically
• AI-mediated adapters reduce interoperability costs