The internet is undergoing a silent revolution.

For decades, browsers have been designed for human eyes and fingers; then clicks, scrolls, buttons, and beautifully laid out pages. But a new kind of user is emerging, one that doesn’t care about pixels or animations. These new users are purely digital - language models operating as autonomous actors across the web. These agents are interacting with websites in ways fundamentally different from humans. This shift marks the birth of two internets: the Seen Browser, built for people, and the “Shadow Browser”, built for machines.

AI browser agents represent a foundational shift in how the web is navigated and understood. These agents are not just passively consuming content either. They are goal-directed, retrieving and synthesizing information, executing workflows, and even taking actions on behalf of users. Unlike traditional crawlers, they bring context, memory, and intent to every interaction. Recent research has started to map out this new frontier. In “WebArena: A Realistic Web Environment for Building Autonomous Agents” researchers introduce an environment where even top-tier models like GPT-4 achieve only a 14.41% task success rate, compared to human performance at 78.24%. The gap is wide, but the progress is fast—and the implications are massive.

Other studies highlight just how different these agents behave. The “WebGames” benchmark shows that while general-purpose agents can solve simple tasks, they struggle with real-world variability in websites, requiring more robust reasoning and planning capabilities. Meanwhile, “Beyond Browsing” explores the power of combining traditional web interfaces with APIs, finding that hybrid agents outperform purely browser-based ones by creating more efficient and reliable pathways for data interaction.

At the center of all this is the concept of the Shadow Browser; the invisible layer that AI agents rely on. It’s made up not of pixels but of structured data, clean HTML, semantic tags, and accessible APIs. For these agents, a visually stunning interface doesn’t mean much if anything at all; what matters is the underlying organization and accessibility of information. Structured metadata, clearly exposed endpoints, and navigable page hierarchies are what drive performance for these agents. Web developers are now facing a dual imperative: designing for people and machines. And like mobile-first design before it, this will become an unavoidable standard.

Security concerns are also front and center. The rise of AI agents introduces new vectors of vulnerability. In “GuiltyAgents” researchers find that agents embedded within browsers are significantly more vulnerable to jailbreak attempts. They found that 46.6% of tested scenarios led to successful jailbreaks, compared to zero for standalone LLMs. These aren’t just research edge cases. As agents proliferate, they’ll be interacting with sensitive systems and data, making security and observability critical components of the emerging stack.

The following early stage startups are working towards shaping this AI-native browsing future: 

• HUD.so is rethinking the operating system layer for AI agents, enabling task automation not via tabs and apps, but via intent. It offers a clean abstraction layer between users and the chaos of the web, mediated entirely through agents

• Browser Use is enabling developers to build programmable browsing behavior into their software. Think of it as an SDK for controlling the Shadow Browser, turning traditional web actions into structured, repeatable workflows 

• Browserbase, which provides programmable, headless Chromium environments that allow AI agents to interact with the web in a controlled, scalable, and observable way. This is essential infrastructure for building agentic systems that navigate the invisible internet 

• Cua AI enables AI agents to safely interact with the hidden web by running each session in isolated Docker containers, providing secure, scalable, and sandboxed browser environments for autonomous tasks

These startups represent a broader design shift: from “read and click” to “query and act.” AI agents don’t care about aesthetics; they care about legibility. Basically, can they extract the right data, interpret it correctly, and move to the next task without a hitch? The tools being built today will form the backbone of this new ecosystem, where websites are consumed less by humans directly, and more by agents acting on behalf of humans.

The Market Opportunity

Additionally, the Shadow Browser potentially represents a new software spend category. Just as businesses invested billions to become mobile-first or cloud-native, they will now need to become agent-ready. 

We estimate this market could easily exceed $20–30 billion annually in the next 5–7 years. If you consider that over 200 million websites operate in commercial capacities globally. If just 10% of these businesses spend an average of $10,000–$20,000 annually on agent-accessible infrastructure (e.g., structured markup implementation, API gateways, RAG pipelines, and agent-UX testing), you’re already looking at a $20B market. This figure doesn’t include adjacent spending on LLM-native analytics, agent monitoring platforms, or security products. All of these could drive total spend even higher. For comparison, the SEO tools market alone surpassed $6B in 2023; the agentic web is in a position to be larger and likely more foundational.

As agent-based browsing becomes a default in both consumer and enterprise environments, every company with a digital footprint will be forced to ask: Is our business accessible not just to users—but to their agents? The answer will increasingly define discoverability, competitiveness, and revenue.

How Businesses Will Have to Adapt

This paradigm shift will be meaningful and it has immediate and material implications for how businesses design, structure, and operate their digital presence. As AI agents become primary consumers of web content, businesses will have to think beyond human-centric UX and into machine-oriented accessibility.

For one, search engine optimization (SEO) will fundamentally change. LLM-powered agents don’t “search” in the traditional sense, they synthesize answers, not links. Businesses that rely on discoverability through traditional keyword-driven SEO may find themselves increasingly invisible to AI agents unless they provide clear, structured content. Thought leadership into LLM information extraction and grounding like that in “WebArena” and “WebAgent” shows how agents favor structured layouts, easily parsable tables, and consistent navigation paths.

Second, APIs will become the front doors of commerce. In “Beyond Browsing,” hybrid agents that use APIs instead of traditional browser actions complete tasks significantly faster and more reliably. This means companies will need to expose API endpoints not just for power users or integrations, but as primary interfaces for machine interaction. Sites without well-documented APIs or agent-friendly documentation will fall behind in accessibility. A great example would be similar to how Flash-based sites became obsolete overnight during the mobile revolution.

Third, analytics and KPIs will shift. Traditional web metrics like bounce rate and time-on-page will matter less in an agent-explored world. Instead, success will be defined by how quickly and accurately an AI agent can complete a user task. Businesses will need tools to debug, observe, and optimize these agent journeys and what might be called agent experience (AX). This is a greenfield opportunity for startups.

Finally, businesses must invest in semantic clarity. Schema.org markup, JSON-LD, and structured HTML elements aren’t just for Google anymore, they’re table stakes for agent comprehension. Just as responsive design became the norm post-2010, “agent-readable” design will be the new baseline.

What We’re Looking For: The Future of the Web

From an Exceptional Capital perspective, the bifurcation of the web is a defining moment. We are watching the formation of a new digital infrastructure. This new infrastructure will be one where companies need to build observability tools not for users, but for agents. Debugging AI behaviors, securing their interactions, and optimizing for non-human consumption will require entirely new toolchains. There is immense opportunity in funding the picks and shovels of the Shadow Browser age: developer tools for AI-native UX, new shift left security products, analytics for agent behavior, and middleware that bridges LLMs and traditional web systems.

What excites us is not just the infrastructure, it’s the chance to reimagine the web itself. When agents become our proxies, surfing and searching on our behalf, websites stop being destinations. They become APIs. They become instructions. They become platforms not for persuasion, but for execution.

The web was built for human users. Now, it’s being rebuilt for machines that act on our behalf. This isn’t just a UX evolution, it’s a redefinition of who the internet is for. As investors, we’re backing the teams who see this future clearly: those who understand that every site is about to become two sites: one for you, and one for your agent.

References & Works Cited

• Chen, T., Yang, M., Lee, S., Zhang, R., Wang, S., Zhang, Y., … & Kong, L. (2023). WebArena: A Realistic Web Environment for Building Autonomous Agents. arXiv. https://arxiv.org/abs/2307.13854

• Xiong, J., Wang, J., Bao, X., Lin, S., Wang, Y., Du, Z., … & Jiang, M. (2024). WebGames: A Benchmark Suite for Evaluating General-Purpose Web-Browsing Agents. arXiv. https://arxiv.org/abs/2502.18356

• Chen, Y., Liu, J., Liu, S., Yang, L., Wang, L., Li, Y., & Chen, M. (2024). Beyond Browsing: Can Agents Take Web Actions via APIs? arXiv. https://arxiv.org/abs/2410.16464

• Liu, Y., Liu, S., Yang, C., Deng, J., Ren, K., Wang, Y., … & Liu, Y. (2024). GuiltyAgents: A Study of Jailbreaks on Web-based Autonomous Agents. arXiv. https://arxiv.org/abs/2502.20383

• Yang, X., Zhang, X., Tan, X., Lin, X., & Shi, B. (2023). WebAgent: Interleaving Natural Language and Web Browsing Commands for Autonomous Web Navigation. arXiv. https://arxiv.org/abs/2308.11484

• Statista. (2023). Global number of websites from 1991 to 2023. https://www.statista.com/statistics/273963/number-of-websites-worldwide/

• Research and Markets. (2023). SEO Software Market - Global Outlook & Forecast 2023-2028. https://www.researchandmarkets.com/reports/5780791/seo-software-market-global-outlook-and-forecast

• Gartner. (2023). Forecast Analysis: APIs Will Drive Modern Web Architectures. Gartner, Inc.

• McKinsey & Company. (2021). The state of AI in 2021. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2021