“Unbrowse Agentic Web”
Date: 19 November, 2025 (Wednesday)
Time: 02:00 PM to 03:00 PM SGT
Presenters: Lewis Tham – NUS undergrad student
This session introduces Unbrowse, a compatibility layer that enables autonomous agents to interact seamlessly with the open internet. We will explore the practical challenges encountered while building this system—from handling diverse web environments to ensuring reliability and safety at scale. The discussion will also highlight key lessons learned, design considerations, and emerging opportunities for agent-based automation. Participants will gain an inside look at how Unbrowse bridges the gap between agents and real-world web interfaces.
Problem
The pitch argues that the internet is currently fragmented and difficult for AI agents to use effectively:
Data and services are locked behind siloed platforms and restricted APIs.
AI agents often rely on GUI-based automation (screen scraping), which is slow and breaks when interfaces change.
User interactions generate valuable insights, but most of that value is captured by large corporations, not users.
These limitations prevent AI agents from scaling across the web.
Solution
Unbrowse proposes a network-level wrapper that converts web activity into structured “abilities” that agents can reuse:
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Intercepts network packets instead of relying on UI automation.
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Converts real user sessions into reusable agent tools.
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Builds a universal index of abilities that agents can discover and combine.
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Uses recommendation systems to match agent intentions with optimal execution paths.
How It Works
1. Capture & Upload – A browser extension records web sessions (credentials remain local).
2. Derive Abilities – Server processes sessions into reusable agent tools.
3. Discover & Run – Agents query the index and execute workflows via proxies.
4. Earn & Evolve – Users earn rewards when their contributed abilities are used
Key Innovations
- Network-Level Precision: Uses packet-level data instead of GUI automation for higher reliability.
- Collective Intelligence: Aggregates user behavior to produce broader insights and trends.
- Unlimited Discoverability: Agents can dynamically find and combine tools.
- User Monetization: Contributors earn rewards when their data-derived abilities are adopted.
Market Opportunity
AI agents market projected to grow from $5–7B in 2025 to ~$42B by 2030.
Year-1 goals:
10,000 users
50,000 indexed abilities
~$1M ARR from SaaS plus ~$500K in token fees.
Vision
Unbrowse positions itself as infrastructure for the “agentic internet”, enabling AI agents to interact with the web seamlessly while allowing users to earn value from their interactions.