Why AI Agents Will Eat The Web (And How Your Business Should Prepare)

Why AI Agents Will Eat The Web (And How Your Business Should Prepare)

The future of digital experience is shifting from browsing websites to engaging with AI agents that know precisely what you need, when you need it. In the coming years, a significant portion of customer-business interactions will bypass traditional websites completely in favor of AI agents that pull content directly from headless systems through standardized protocols.

This emerging trend is already showing results. Early adopters in banking, e-commerce, and healthcare are seeing higher conversion rates and reductions in operational costs compared to traditional web experiences. The question for business leaders is not whether this transformation will happen, but how to position your organization to benefit from it.

The invisible revolution reshaping digital business

For decades, the website has been the digital storefront—the place where customers discover, shop, and interact with businesses. This familiar paradigm is being challenged by three converging technologies creating something entirely new: a web experienced primarily through AI agents rather than browsers.

Think about it: when was the last time you visited a website to find information that could have been delivered through a conversation?

Many of us already reach for ChatGPT instead of Google for information-seeking, and this behavior is accelerating as AI agents gain the ability to access live systems rather than just static knowledge.

This shift represents a fundamental change in how digital information and services are delivered. Instead of visiting websites directly, users increasingly rely on AI agents that access content and systems on their behalf, delivering precisely what's needed without requiring navigation through pages and forms.

The technical foundation enabling agentic web experiences

Three key technologies are driving this transformation:

1. Headless architectures: Liberating content from its presentation prison

Headless content platforms separate backend systems (where content lives) from frontend interfaces (how users experience it). This architecture delivers content via APIs rather than pre-rendered pages, enabling the same content to be used across multiple touchpoints.

Companies adopting headless architectures report significant benefits—Burberry accelerated content creation across 11 languages and achieved 80% faster translation workflows compared to their previous CMS.

For businesses, headless means your content becomes truly omnichannel—available wherever your customers are, including through AI agents. The key advantage isn't just technical flexibility but business agility—updating content once and having it instantly available across all channels.

In the near future, SaaS headless CMS platforms will continue to dominate, but looking further ahead, we may see even these platforms replaced by simple data repositories with AI agents using MCP to both retrieve and manage content directly—eliminating middleware layers entirely.

2. Model Context Protocol: The universal connector for AI

The Model Context Protocol (MCP), introduced by Anthropic in late 2024 and quickly adopted across the industry, solves a critical problem: how AI agents connect to the systems where data actually lives.

MCP creates a standardized way for AI applications to access external tools, data sources, and systems. It works like a "USB-C port for AI"—providing a universal connector between AI assistants and any data source or service they need to access.

Major AI providers including Anthropic, OpenAI, and Google DeepMind have adopted MCP, with an ecosystem of open-source connectors for everything from Google Drive to PostgreSQL databases. Companies like Block and Apollo have integrated MCP into their systems, while development tools including Cursor, GitHub Copilot, Zed, Replit, and Sourcegraph leverage MCP to enhance their platforms. Headless CMS platforms like Sanity have also implemented MCP servers, making their content directly accessible to AI agents.

The MCP specification is still undergoing heavy development, yet we're already witnessing remarkably broad adoption across the ecosystem. As fully remote MCP capabilities mature, we'll likely see the emergence of MCP registries—catalogs of available MCP services that agents can discover and utilize.

This will enable AI agents to autonomously enable and call appropriate MCPs based solely on user requests, without requiring manual configuration. Imagine asking your AI assistant about local restaurants, and it automatically activates the appropriate mapping, review, and reservation MCPs to fulfill your request seamlessly.

3. Persistent memory: Giving AI agents context beyond the moment

While large language models have made remarkable progress in reasoning capabilities, they've historically been constrained by their inability to maintain information between sessions—essentially forgetting who you are and what you've discussed once a conversation ends.

Recent breakthroughs in persistent memory systems enable AI agents to maintain context over time. Notable examples include OpenAI's "Persistent Memory" feature for ChatGPT, which allows the model to remember important details about users' preferences and past interactions.

These systems use hierarchical memory structures:

• Working memory for immediate context

• Short-term memory for current sessions

• Long-term memory for persistent knowledge

For businesses, AI agents with memory create a fundamental shift in customer relationships. Instead of treating each interaction as new, these systems maintain understanding of preferences, history, and context across multiple sessions—creating truly personalized experiences that improve over time.

How AI agents are replacing websites across industries

This technological foundation is already transforming customer experiences across industries:

Banking without websites

In banking, companies are deploying AI agents that replace traditional banking websites with conversation-based interfaces. These agents integrate with core banking systems through MCP-like protocols and use persistent memory to maintain customer context.

Banks report significant benefits:

• 80% automation of routine inquiries

• 41% reduction in documentation time

• 30-40% reductions in operational costs

Most importantly, these systems eliminate the need for customers to navigate complex banking websites, instead delivering personalized service through natural conversations.

The e-commerce agent revolution

E-commerce is seeing similar transformation. Stripe released its Agent Toolkit in 2024, enabling AI agents to execute payment transactions through API integrations via MCP. Shopify's headless commerce architecture allows businesses to create AI agents that manage product catalogs, handle ordering, and provide personalized shopping experiences.

The evidence is clear: businesses using headless commerce architectures report 42% higher conversion rates, and companies using AI agents see up to 35% higher conversion rates compared to traditional website experiences. Amazon reports that 35% of its revenue now comes from AI agent recommendations.

Numerous companies are building specialized e-commerce agents that can respond to customer queries, offer personalized recommendations, and guide customers through purchasing decisions like human agents. These systems don't just answer questions—they drive sales and recover abandoned carts.

The business opportunity: Optimizing for the agentic web

For businesses, this transition presents both opportunities and challenges:

Near-term opportunity: Making your data "agent-ready"

The most immediate opportunity is leveraging headless architectures to make your data and services available to AI agents. This doesn't require reinventing your entire digital presence—it means exposing your existing content and functionality through APIs that can be accessed by AI ecosystems.

This approach offers substantial advantages:

• Extended reach as your content becomes accessible through various AI interfaces

• Improved operational efficiency through automation

• New business models around agent-ready content and services

Several strategies can help businesses prepare:

1. 1

   Conduct an API readiness assessment to evaluate your systems

2. 2

   Implement headless architecture for customer-facing content

3. 3

   Develop agent experience guidelines for how AI should interact with your systems

4. 4

   Start with specific, high-value use cases rather than attempting wholesale transformation

Long-term caution: Navigating the risks of middleman elimination

The longer-term picture includes significant risks that businesses must navigate:

Data control challenges: As AI agents mediate more customer interactions, questions emerge about who controls user data and relationships. Super-apps could develop comprehensive user profiles across services, raising privacy concerns and concentrating market power.

Direct provider access risks: AI agents may bypass traditional aggregators by communicating directly with service providers. Customer relationships may shift from brands to the AI agents representing consumers, fundamentally changing value chains.

Defensive strategies:

• Develop proprietary agents that represent your brand values

• Implement API governance with clear terms of service

• Focus on services agents cannot easily replicate

• Form strategic partnerships with agent platforms

• Build direct customer trust through transparency and value

Organizational transformation for an agentic future

Preparing for this shift requires significant organizational changes:

New roles are emerging:

• Agent Experience (AX) Designers optimize digital environments for AI agent interaction

• AI Orchestrators manage coordination between multiple specialized agents

• Agent Governance Officers ensure responsible agent deployment

Companies are restructuring: Salesforce rebranded parts of its platform as "Agentforce," focusing on creating and managing AI agents. Microsoft reorganized to create dedicated teams focused on the "open agentic web."

Investment is accelerating: Global venture capital funding for generative AI continues to grow, with an increasing focus on agentic capabilities.

Conclusion: Preparing for the invisible web

The transition to an agentic web represents both opportunity and challenge. Organizations that proactively adapt by implementing headless architectures, reorganizing for agent integration, and developing strategies against middleman elimination will be better positioned to thrive.

The key imperatives for businesses are clear:

1. 1

   Make your content and services accessible through standardized APIs

2. 2

   Develop new skills and roles focused on agent experience and governance

3. 3

   Consider how to maintain relevance in value chains increasingly mediated by AI

The agentic web is evolving rapidly, with significant developments expected in the next 1-3 years. While the traditional web isn't disappearing overnight, forward-thinking businesses are already preparing for a future where AI agents become the primary channel through which customers experience their products and services.