It's November 2028. Maya's personal AI agent quietly handles her holiday shopping, easily navigating dozens of e-commerce sites. Unlike the clunky chatbots of 2024, her agent seamlessly parses product specifications, compares prices, and makes purchase decisions based on her preferences.
"The boots for your sister," it explains, "are from that sustainable brand you both discussed last month - I found them at 20% off and confirmed they'll arrive before your family gathering." What would have taken Maya hours of manual searching now happens automatically, thanks to a web rebuilt for agent-first interaction.
—> The future, three years from now.
As we approach the end of 2024, a new paradigm shift is emerging in how we build and interact with the internet. With rapid advances in AI reasoning capabilities, tech giants and innovative startups alike are racing to define the next evolution of digital interaction: AI agents, .
Google, Apple, OpenAI, and Anthropic have all declared AI agents as their primary focus for 2025. This transformation promises to be as significant as the web and mobile revolutions were and represents perhaps the most natural interface for LLM-powered technology, far more intuitive and capable than the chatbots that preceded it.
In the recent No Priors Podcast, Nvidia’s CEO Jensen Huang stated that "there's no question we're gonna have AI employees of all kinds” that would "augment every single job in the company”.
Moreover, Gartner projects that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% today, enabling 15% of day-to-day work decisions to be made autonomously. This rapid adoption mirrors the mobile revolution of the early 2010s but with potentially more far-reaching implications for how we interact with digital services.
AI agents: Automation and intelligent assistance (2025 guide)
AI agents are intelligent software entities designed to operate autonomously and achieve specific goals.
What sets AI agents apart?
While there's ongoing debate about what an AI Agent is, at its core, what sets agents apart from traditional software is their ability to autonomously plan and adapt.
Unlike rule-based systems that follow predetermined paths, agents can formulate strategies, execute them, and—most importantly—adjust their approach based on outcomes and changing circumstances. Think of them as digital assistants that don't just follow a script, but actually reason about the best way to achieve your goals.
If a planned action fails or yields unexpected results, an agent can reassess and chart a new course, much like a human would. This flexibility and autonomous decision-making capability marks a departure from traditional software, which can only respond in pre-programmed ways.
The use of tools
Central to agents' capabilities is their sophisticated use of tools. Much like a handyman who knows when to use a screwdriver versus a hammer, agents must determine which tools to use, when to use them, and how to use them effectively.
For instance, when helping you plan a trip, an agent might first use a calendar tool to check your availability, then a flight search API to find options, and finally a weather service to ensure you pack appropriately. The key isn't just having access to these tools — it's the agent's ability to reason about their use and orchestrate them intelligently to accomplish complex tasks.
This article was originally published here at AI Tidbits, where you can read more of Sahar's fascinating perspectives on AI-related topics.
From mobile-first to agent-first
Remember when 'www' stood for something closer to 'Wild Wild West' than 'World Wide Web'? The early 2000s internet was an untamed digital frontier, where users navigated through a maze of pop-ups, fought off malware, and relied on bookmarked URLs just to find their way around.
The early 2010s, when mobile exploded, weren’t that different as businesses scrambled to make their websites mobile-responsive. That shift wasn't just about resizing content for smaller screens–it fundamentally changed how we approached web design, user experience, and digital strategy. It created a whole new field of website and mobile optimization: choosing the best colors and text copy to increase traffic, conversion rates, and stickiness.
The agentic AI inflection point
Today, we stand at a similar inflection point with AI agents.
Just as mobile-responsive design emerged from the need to serve smartphone users better, "agent-responsive design" is emerging as websites adapt to serve AI agents. But unlike the mobile revolution, which was about accommodating human users on different devices, the agent revolution requires us to rethink our fundamental assumptions about who – or what – is consuming our digital content.
In this agent-first era, websites will undergo a dramatic transformation. Gone are the days of flashy advertisements, elaborate typography, and resource-heavy images — elements that consume bandwidth but provide little value to AI agents.
Instead, we're moving toward streamlined, efficient interfaces that prioritize function over form. These new websites will feature minimalist designs optimized for machine parsing, structured data layers that enable rapid information extraction, standardized interaction patterns that reduce processing overhead, and resource-efficient components that minimize token usage and computation costs.
This evolution extends beyond traditional websites. Mobile applications are already being reimagined with agent-interaction layers, as evidenced by recent novel methods like Apple's Ferret-UI 2 and CAMPHOR, enabling seamless agent navigation of mobile interfaces while maintaining human usability.
Google and Microsoft also invest in this space, as demonstrated in their recent papers AndroidWorld and WindowsAgentArena, respectively. Both are fully functional environments for developers to build and test agents.
The incentives are becoming clear: optimize for agents, and you'll unlock new channels of engagement and commerce. Ignore them, and you risk becoming invisible in the emerging agent-first internet.
What is Agent Responsive Design?
At its core, agent-responsive design represents a radical departure from traditional web design principles. Instead of optimizing for human visual perception and engagement, websites must provide clear, structured interfaces that agents can efficiently navigate and interact with.
This transformation will likely unfold in two phases:
Phase 1: Hybrid optimization
Initially, websites will maintain dual interfaces: one optimized for human users and a "shadow" version optimized for agents. This agent-optimized version will feature:
- Enhanced semantic markup with clear structure and purpose
- Unobfuscated HTML that welcomes rather than blocks automated interaction
- Well-defined aria-label labels and metadata to help agents choose and interact with the right UI components
- Direct access to knowledge bases and documentation by exposing information beyond what’s visible on the “website interface”, giving the querying agents access to their RAG to easily retrieve information such as refund policy or answer questions the agent has based on their help docs. Also, after being authenticated, providing easy access to user-related information such as last purchases or stored payment methods.
- Streamlined authentication and authorization protocols
Phase 2: API-first architecture
The second phase will move beyond traditional UI components, focusing on exposing clean, well-documented APIs that agents can directly interact with. Consumer websites like Amazon, TurboTax, and Chase will:
- Provide clear documentation of available tools and capabilities. The agent will leverage its reasoning engine and the task the human delegated to plan the tools and sequence that it needs to use.
- Offer structured workflows with explicit input/output specifications
- Enable direct access to business logic and user data
- Support sophisticated authentication mechanisms for agent-based interactions
AI agents will make traditional A/B testing obsolete
In an agent-first world, the traditional approach to A/B testing becomes obsolete. Instead of testing different button colors or copy variations for human users, companies like Amazon will need to optimize for agent interaction efficiency and task completion rates.
These A/B tests will target similar metrics as today: purchases, sign-ups, etc., employing LLMs to generate and test thousands of agent personas without the need for lengthy user testing cycles.
This new paradigm of testing will require new success metrics such as:
- Model compatibility across different AI providers (GPT, Claude, etc.) - each language model has its own nuances. Optiziming can help businesses squeeze a few more percentage points for conversion, bounce rate, etc.
- Task completion rate for the human-delegated task at hand, like purchasing a product or subscribing to a newsletter
- Token efficiency and latency optimization, enabling lightning-fast interactions while minimizing computational overhead and associated costs
- Authentication and security protocol effectiveness, ensuring robust protection while maintaining frictionless agent operations
The competitive landscape in this new era will be shaped significantly by model providers' unique advantages. Companies like OpenAI and Google, with their vast user interaction data, will possess an inherent edge in creating agents that deeply understand user preferences and behaviors. However, this also creates an opportunity for innovation in the form of universal memory and context layers, like what mem0 is pitching with their recently released Chrome extension—systems that can bridge different models, devices, and platforms to create a cohesive user experience.
Drawing from Sierra's τ-bench research, we can anticipate the emergence of standardized benchmarks for measuring agent-readiness across verticals and task types, similar to how we currently measure mobile responsiveness or page load times.
New discovery protocol - Agent Engine Optimization (AEO)
Just as websites evolved from manually curated directories to sophisticated search engine optimization, the agent era demands a new discovery mechanism. The question isn't just about findability—it's about actionability: how do agents identify and interact with the most relevant and capable digital services?
In 2005, Google introduced the Sitemap protocol to improve search engine crawling efficiency, enable discovery of hidden content, and provide webmasters with a standardized method for communicating site structure and content updates to search engines. What is the Sitemap equivalent for AI agents?
Just as SEO emerged to help websites become discoverable in search engines with Google’s inaugural PageRank algorithm, Agent Engine Optimization (AEO) will become crucial for visibility in an agent-first web. Back in Aug 2023, I called it Language Model Ranking Optimization.
This new protocol will go beyond traditional sitemaps, providing agents with structured information about websites:
- Available services and capabilities like signing up, placing an order, booking a flight seat
- Authentication requirements - what actions require authentication
- Data schemas and API endpoints - what data does each action/endpoint need? What is mandatory vs. optional?
- Privacy and security protocols - how information is being stored
- Service level agreements like refund and shipping guidelines and data retention policy
Exposing such information will become a standard feature in website builders like Shopify and Wix, much like mobile responsiveness is today. These platforms will automatically generate and maintain agent-interaction layers, democratizing access to the agent-first economy for businesses of all sizes.
Companies will need to optimize not just for search engines but for an emerging ecosystem of agent directories and registries that help autonomous agents discover and interact with digital services.
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