The rise of AI browsers introduces 'agents' that automate tasks like research and form submissions. To capture leads from these agents, websites must feature simple, easily parsable forms and navigation, creating a new dimension of user experience focused on machine readability.

Websites now have a dual purpose. A significant portion of your content must be created specifically for AI agents—niche, granular, and structured for LLM consumption to improve AEO. The human-facing part must then evolve to offer deeper, more interactive experiences, as visitors will arrive with their basic research already completed by AI.

AI agents are becoming the dominant source of internet traffic, shifting the paradigm from human-centric UI to agent-friendly APIs. Developers optimizing for human users may be designing for a shrinking minority, as automated systems increasingly consume web services.

In this software paradigm, user actions (like button clicks) trigger prompts to a core AI agent rather than executing pre-written code. The application's behavior is emergent and flexible, defined by the agent's capabilities, not rigid, hard-coded rules.

While language models are becoming incrementally better at conversation, the next significant leap in AI is defined by multimodal understanding and the ability to perform tasks, such as navigating websites. This shift from conversational prowess to agentic action marks the new frontier for a true "step change" in AI capabilities.

For decades, the goal was a 'semantic web' with structured data for machines. Modern AI models achieve the same outcome by being so effective at understanding human-centric, unstructured web pages that they can extract meaning without needing special formatting. This is a major unlock for web automation.

For many knowledge workers, the browser is their primary IDE. AI tools that operate as embedded extensions can leverage the real-time context of a webpage, combine it with a user's broader work data, and provide powerful, in-the-moment assistance without forcing a context switch.

A new software paradigm, "agent-native architecture," treats AI as a core component, not an add-on. This progresses in levels: the agent can do any UI action, trigger any backend code, and finally, perform any developer task like writing and deploying new code, enabling user-driven app customization.

For years, businesses have focused on protecting their sites from malicious bots. This same architecture now blocks beneficial AI agents acting on behalf of consumers. Companies must rethink their technical infrastructure to differentiate and welcome these new 'good bots' for agentic commerce.