Executive Summary

The Infrastructure Gap in AI Agent Economy

Artificial intelligence has evolved from text generation tools into autonomous systems capable of real-world action. Large language models demonstrate human-level reasoning across domains, yet a fundamental infrastructure barrier prevents their practical deployment: AI agents cannot reliably interact with the web.

Contemporary web architecture was designed for human visual consumption through HTML, CSS, and JavaScript. AI agents require structured data, verifiable provenance, and automated execution capabilities that existing web infrastructure does not provide. This disconnect represents the primary bottleneck preventing the emergence of an autonomous agent economy.

The Problem

Structural Incompatibility

The web presents information through visual interfaces optimized for human perception. AI agents operate on structured data formats. Current approaches to bridging this gap suffer from fundamental limitations:

LLM-based HTML parsing produces inconsistent output schemas. Research demonstrates that even state-of-the-art models exhibit schema compliance rates varying from 0% to 100% depending on task complexity. Production deployments require predictable data structures.

Traditional web scraping tools face detection and blocking by modern anti-bot systems. Browser fingerprinting, behavioral analysis, and IP reputation systems identify and restrict automated access. Centralized headless browser services provide limited mitigation at substantial cost.

API-based access covers less than 1% of web content. The majority of websites do not provide programmatic interfaces. Where APIs exist, they impose rate limits, usage fees, and functional restrictions that constrain agent capabilities.

Verification Impossibility

No existing system provides cryptographic proof of web data provenance. Applications requiring verified data sources—financial compliance, legal documentation, regulated industries—cannot utilize web data retrieved through current methods. The inability to prove that data originated from a specific server at a specific time eliminates entire use cases for autonomous agents.

Centralized Infrastructure Risks

The web automation market exhibits concentration among a small number of providers. This centralization creates:

• Single points of failure: Infrastructure outages eliminate service availability
• Cost inefficiency: Limited competition enables premium pricing without performance differentiation
• Censorship vulnerability: Policy changes by providers can terminate service access
• Scalability constraints: Processing capacity limited by centralized infrastructure investment

The Solution

Sela Network is a decentralized web-interaction layer that enables AI agents to see, understand, and act on the web through three integrated technical components:

Distributed Browser Network

A global network of distributed browser nodes executes actual web sessions. Unlike centralized browser automation services relying on cloud-hosted instances, Sela leverages real user browsers distributed across 150+ countries.

This decentralized physical infrastructure (DePIN) approach provides:

• Bot detection resistance: Real user browsing patterns bypass modern anti-bot systems. Residential IP addresses, authentic browser fingerprints, and natural interaction patterns eliminate detection signals.
• Geographic distribution: Local nodes access region-restricted content and minimize latency through proximity routing.
• Horizontal scalability: Network capacity grows linearly with node participation without centralized bottlenecks.
• Resilience: No single point of failure. Service continues despite individual node outages or regional restrictions.

Each node operates through a Chrome extension or standalone application, processing requests while maintaining session isolation through browser-native sandboxing mechanisms.

Semantic Interpretation Engine

The web was designed for human visual consumption. AI agents require structured data formats. Sela’s Semantic Interpretation Engine combines Large Language Models (LLMs) with DOM parsing to transform visual web content into structured JSON.

Hybrid parsing architecture analyzes both visual presentation and underlying document structure. LLMs identify buttons, forms, and interactive elements by appearance. DOM parsing extracts attributes, relationships, and semantic metadata. This combination provides parsing accuracy while maintaining resilience against layout changes.

Schema normalization enforces consistent output conforming to Schema.org standards. Unlike LLM-based parsing that generates variable schemas for identical pages, Sela produces deterministic, predictable data structures. This enables stable agent development without constant schema adaptation.

Self-healing selectors automatically adapt to UI changes without service interruption. When website layouts change, the system detects modifications, identifies equivalent elements through visual and structural matching, and updates selectors automatically. This eliminates the brittleness that characterizes traditional web scraping implementations.

Cryptographic Verification Layer

The fundamental challenge of web data is trust. Sela implements zk-TLS (zero-knowledge Transport Layer Security), a cryptographic protocol that proves data provenance and integrity without exposing sensitive information.

The zk-TLS protocol operates through multi-party computation:

1. A TLS session is established between the browser node and target server
2. Session keys are split between the prover (node) and verifier (notary service)
3. Both parties collaboratively decrypt responses without either having full access
4. The notary generates cryptographic proof of the data’s origin and timestamp
5. Zero-knowledge proofs enable verification without revealing underlying data

This enables applications in finance, legal compliance, and regulated industries where data authenticity is essential. Every web interaction can generate an audit trail with cryptographic attestation, providing compliance with regulatory requirements and legal admissibility.

Technical Architecture

Sela Network operates as a three-layer stack:

Layer 1: Web Transport Layer provides the physical infrastructure for web access through distributed browser nodes, residential proxy networks, and session management systems. This layer handles bot detection avoidance, dynamic JavaScript rendering, and complex authentication flows.

Layer 2: Semantic Interpretation Layer transforms visual web content into structured data through LLM-based UI recognition, DOM-to-JSON conversion, and automated schema normalization. Self-healing mechanisms maintain functionality despite website changes.

Layer 3: Verifiability Layer generates cryptographic proofs of data provenance through zk-TLS implementation, notary services, and proof aggregation systems. This layer enables verifiable web actions with legal and compliance validity.

Market Opportunity

AI agent market growth projections indicate expansion from $5.4 billion in 2024 to $50-93 billion by 2030-2032, representing a compound annual growth rate exceeding 45%. This growth is driven by enterprise adoption, with 85% of companies expected to implement AI agents by end of 2025.

DePIN (Decentralized Physical Infrastructure Networks) markets demonstrate similar trajectory, growing from $30-50 billion current valuation to projected $3.5 trillion by 2028. This represents expansion from less than 0.1% of the global infrastructure market to meaningful penetration.

Sela Network operates at the intersection of these convergent trends. AI agents require web interaction infrastructure. DePIN provides the decentralized coordination mechanism. The combination addresses a fundamental infrastructure gap in an expanding market.

Strategic Differentiation

Cryptographic verification: Sela is the only web interaction platform providing cryptographic proof of data provenance through zk-TLS implementation. This enables use cases in regulated industries and compliance contexts that cannot utilize unverified data sources.

Hybrid parsing architecture: The combination of vision-based UI understanding with structural DOM analysis provides resilience against layout changes while maintaining parsing accuracy. Self-healing mechanisms eliminate maintenance overhead.

True decentralization: Unlike proxied access to centralized browser farms, Sela operates as a peer-to-peer network without dependency on single providers. Geographic distribution and node redundancy ensure service continuity.

Economic sustainability: Token-based incentive alignment creates a self-reinforcing cycle where increased agent usage drives node growth, which improves network capacity and reliability. This contrasts with centralized models requiring continuous infrastructure investment.

Use Cases

Autonomous Trading Systems

AI agents analyze real-time market data from exchanges, news sources, and social platforms, then execute trades through web interfaces. Cryptographic proofs provide auditable records of data sources and execution times for compliance verification.

Cross-Platform Commerce

Agents compare prices, inventory, and shipping options across multiple e-commerce platforms, then execute purchases through the optimal channel. Decentralized nodes bypass geographic restrictions and bot detection systems that limit centralized approaches.

Regulatory Compliance Monitoring

Systems continuously monitor government websites for regulatory updates, extract structured information from legal documents, and generate cryptographically-verified records of publication timestamps and content. This enables automated compliance reporting with verifiable audit trails.

Research and Intelligence

Agents systematically collect data from diverse web sources while maintaining provenance chains for all information. Verifiable timestamps and source attribution support research integrity and fact-checking workflows.

Network Participants

AI Agent Developers

For developers building autonomous systems with frameworks like LangChain or AutoGPT, Sela abstracts web interaction complexity behind a consistent API. Instead of managing browser automation, proxy rotation, CAPTCHA solving, and HTML parsing, developers access structured data through standardized interfaces. Self-healing selectors eliminate maintenance overhead when target websites change.

Node Operators

Individuals and organizations contribute computational resources by running browser nodes. Participants install client software, stake tokens, and earn rewards proportional to their contribution. The decentralized infrastructure model creates opportunities for passive income through resource sharing, similar to other DePIN networks in storage, computing, and bandwidth.

Data Consumers

Enterprises and applications requiring verified web data access Sela’s marketplace for pre-structured datasets and extraction templates. Cryptographic verification enables use in contexts requiring data authenticity guarantees—financial audits, legal proceedings, compliance reporting.

Long-Term Vision

The objective is to establish Sela Network as the foundational operating layer enabling AI agents to reliably use the internet—the HTTP/TCP/IP equivalent for the AI agent era.

In the same way HTTP provided a standard protocol for human web access, enabling the creation of the modern internet economy, Sela aims to provide standardized, verifiable web access for autonomous agents.

This infrastructure enables:

• Autonomous economic agents conducting transactions across web platforms
• Verifiable data provenance for AI training and decision-making
• Decentralized coordination between agents and web services
• Trustless verification of agent actions and outcomes

As AI capabilities expand beyond text generation toward autonomous action, reliable web interaction infrastructure becomes foundational. Current solutions—centralized browser services and brittle scraping tools—cannot scale to support an ecosystem of millions of autonomous agents. Sela Network addresses this infrastructure requirement through decentralized coordination, cryptographic verification, and economic sustainability.

Conclusion

Sela Network provides the infrastructure layer enabling AI agents to interact with the web reliably, verifiably, and at scale. By combining distributed browser networks, semantic interpretation engines, and cryptographic verification protocols, Sela addresses the fundamental barriers preventing autonomous agent deployment.

The convergence of AI agent adoption and decentralized infrastructure creates a market opportunity measured in tens of billions of dollars. Sela’s technical architecture, strategic positioning, and economic model position the network to capture significant value in this emerging ecosystem.