Introduction: Agent Internet—A Clean Network Architecture Reconstructed for Machines

This article proposes the concept of "Agent Internet" to address efficiency and cost issues in AI systems' web information extraction. The core idea is to shift from cluttered web pages designed for humans to clean data layers optimized for agents. It discusses the unfriendliness of the current web to AI, three key solutions (dedicated extraction services, self-hosted search, agent-optimized content formats), the evolution of the business ecosystem, tech stack restructuring, challenges, future vision, and provides practical suggestions for developers.

Background: The Unfriendliness of the Current Web to AI

Necessity of Paradigm Shift

Over the past three decades, the Internet has been designed for humans—with beautiful but bloated UIs. When AI agents become the main visitors, irrelevant content like JS, CSS, and ads in traditional HTML pages causes 70% of token consumption in LLM processing to be wasted on parsing garbage information, increasing costs.

Essence of the Problem

Current web pages are a museum of technical debt, with low content ratio (only about 15%) due to compatibility with old browsers, SEO, and ads. HTML is a presentation-layer language lacking semantic annotations, forcing AI to simulate human visual parsing, which is inefficient and fragile.

Solutions: Three Key Approaches for Agent Internet

1. Dedicated Extraction Layer (Firecrawl Model)

Runs a browser environment to render JS, extracts semantically structured content into clean Markdown, reducing token consumption and crawler complexity. However, it still needs to handle original web pages and anti-crawling measures.

2. Self-hosted Search (SearXNG Path)

A decentralized meta-search engine that aggregates results and provides a unified API, enabling control and privacy. For high-frequency scenarios, its cost is an order of magnitude lower than commercial APIs.

3. Agent-Optimized Content Formats

• Native Markdown: Structured text without redundant styles
• Semantic Annotations: Use Schema.org to mark content types
• API-First: Expose content via API first
• Chunk-Friendly: Pre-split long content into semantic fragments

Evidence: Evolutionary Signals in Business Ecosystem and Tech Stack

Business Ecosystem

Emerging players like Tavily (research-grade search), Perplexica (self-hosted Perplexity), and Jina AI (embedding and reordering) are building agent-native service layers with APIs as interfaces, optimizing accuracy and token efficiency.

Tech Stack Restructuring

The "Agent Stack" is on the rise: the data layer consists of vector storage and semantic indexing; the computation layer includes LLM inference and tool calling; the presentation layer is dialogue flow. "Retrieval as a Service" allows developers to focus on business logic.

Challenges: Practical Trade-offs of Agent Internet

• Legal Compliance: The legal boundaries of large-scale crawling are blurred, with varying attitudes across jurisdictions
• Quality Control: Automated extraction may mistakenly delete key context
• Business Resistance: Ad-driven models are affected by agents skipping ads, which may trigger anti-crawling measures and lawsuits
• Diversity Loss: Risk of centralization due to a small number of extraction services

Future Vision: A Hierarchical Network for Human-Machine Symbiosis

The web will be layered: the human layer retains visual design, while the machine layer is structured, semantic, and API-native. The optimal architecture is "single source, multiple representations", where the same content adapts to different consumers such as humans, AI, and IoT. The Agent Internet is not a replacement for the human web but the next step in evolution, eventually becoming more friendly to humans as well.

Practical Advice: Action Guide for AI Application Developers

1. Audit token consumption in the RAG pipeline; if it exceeds 30%, introduce dedicated extraction services
2. Experiment with self-hosted search (e.g., SearXNG) for high-frequency, sensitive, or long-tail queries
3. Output LLM-ready formats: native Markdown + Schema.org annotations
4. Design chunking strategies: pre-split long content and write summaries
5. Monitor extraction quality and establish a manual sampling inspection mechanism