Preparing for the Open Agentic Web: How Brands Can Thrive with AI Agents and Human Audiences

Preparing for the Open Agentic Web: What Brands Need to Know

The web is changing how people search and interact online. AI agents and answer engines are shifting the landscape, forcing brands to rethink website design, SEO, and performance measurement. This new era demands strategies that address both human users and AI-driven tools.

Google’s search results have evolved. The top spot is often taken by AI Overviews, which provide direct answers and reduce clicks to brand websites. Meanwhile, search is no longer limited to traditional engines. Social media platforms and AI-powered answer engines like ChatGPT and Perplexity now attract growing attention.

Dual-audience websites are becoming the norm. Guill Rodas, CTO at RGA, APAC, points out that AI agents are emerging as a major audience online. This means web design and engineering must evolve to serve two masters: humans and machines. The goal is to deliver content that both can consume effectively.

This shift follows the mobile-first era but challenges traditional web principles. Human readability, SEO for traffic, and click-through rates are losing their dominance. AI Overviews reduce the need for users to visit sites, impacting key metrics like traffic and CTRs. To adapt, websites must be modular and rich in structured data, making it easy for AI agents to access and process information.

Lars Maehler from Publicis Media Hong Kong explains that website design is moving from static, visual layouts to dynamic, query-driven interfaces. AI algorithms don’t browse as humans do—they crawl content for relevance, context, and structured data to answer specific questions. This encourages a Q&A-focused content strategy that directly addresses user intent with clear, concise answers.

Brands need to label products, services, pricing, and create content that speaks to both humans and machines. Jim Yu, CEO of BrightEdge, compares this to the early days of search engines when websites had to shift from purely aesthetic designs to crawler-friendly formats. We’re facing a similar evolution with AI agents today.

AI-Driven Personalisation Becoming the Norm

Personalisation powered by AI is becoming standard. Brands must balance delivering customised content with maintaining consistent identity. Etienne Gautheron of Jellyfish South Korea highlights that modular content systems, tone-of-voice guidelines, and clear brand frameworks help AI tools adapt messaging without causing brand fragmentation.

Internal alignment is key. Brands that rethink content creation and governance from the ground up avoid chaos in personalisation efforts. David Klein from Orange Line stresses locking in voice, tone, and style to keep content cohesive. Real-time data monitoring also helps brands adjust if personalisation drifts off-brand.

AI leverages first-party data from Customer Data Platforms (CDPs) to understand preferences and behaviours. For example, a global fashion brand might present minimalist styles to Gen Z in Japan and vibrant patterns to Brazilian millennials, all while maintaining consistent messaging. Analytics provide insights, and human editors ensure the tone aligns with the brand voice.

Adapting SEO and Content Strategies for AI Agents

New platforms like Gemini, Claude, Perplexity, Deepseek, and OpenAI are becoming gatekeepers. Brands must learn how these AI agents work, the sources they cite, and how they interpret information. SEO is shifting from keyword-focused to conversational, long-tail queries.

For instance, instead of searching “best running shoes,” a voice query might be “What are the best running shoes for marathon training under $100?” Brands need to create content that reflects natural speech patterns. Structured data such as FAQ and how-to schemas are critical for AI to parse and present content.

Zero-click searches, where answers appear directly on the search page, reduce website visits. Maehler advises crafting concise, authoritative content targeting “position zero” or featured snippets. A fitness brand could publish a clear Q&A post on “Top 5 Marathon Shoes for Beginners” to capture voice search and AI citations.

AI bias and attribution pose challenges. High-authority domains dominate AI citations, making it harder for smaller brands. Building E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) through quality backlinks and expert content is essential. Tracking AI-driven traffic with tools like Google Analytics 4 or emerging platforms offering behavioral query insights helps refine strategies.

Brands should monitor trends using tools like Google Trends and Exploding Topics to create timely, relevant content. For example, a surge in “sustainable running gear” queries signals an opportunity for new content.

Decisions about allowing AI to access intellectual property are becoming important. Antonio Panuccio from Enigma notes that referrals from AI will become a new channel to track. Zero-click searches and marketing funnel changes mean shorter website sessions may still lead to high-value sales, as AI matches products to users efficiently.

The Era of the Open Agentic Web

Technologies like agentic AI, generative AI, blockchain, and Web3 offer ways to create web experiences that serve both AI agents and humans. Generative AI enables fast, personalised content at scale. Agentic AI automates interactions and transactions, making websites smarter in real time. Blockchain adds security and builds trust, especially for transactions and data handling. Web 3.0 introduces decentralised, immersive experiences that improve data integration and authenticity.

Imagine a travel site where an AI agent books flights using verified blockchain data while generative AI creates a custom itinerary based on user preferences—all without manual input. This vision is becoming reality.

The challenge is integrating these technologies across channels while aligning with business goals. Microsoft’s vision of the open agentic web sees AI agents performing tasks and decisions across personal and enterprise contexts, changing website design fundamentally.

Emerging protocols like NLWeb aim to expose natural language interfaces natively to AI agents. Using standards like Schema.org and RSS, NLWeb allows websites to provide machine-readable data alongside human content. Mo Cherif from Sitecore compares this shift to how HTML transformed document sharing, calling for shared standards and open-source tools to accelerate adoption.

Measuring AI-Optimised Web Experiences

AI-powered search and summaries are changing consumer behavior, causing a steep decline in traditional click-through rates. Around 80% of consumers now prefer zero-click results, affecting 40% of their information seeking. This disrupts digital marketing approaches focused on driving traffic.

New metrics must include AI agents. Kapil Yadav from Monks says success depends on serving AI agents effectively, enabling them to access and use website data to meet user needs. This includes creating machine-readable content and APIs for key functions.

Traditional metrics like CTR and time-on-page no longer tell the full story. Cherif advises focusing on outcomes, such as how often AI agents select your brand as the preferred answer or action. Platforms like Google’s Search Generative Experience and assistants like Perplexity shift discovery towards action, requiring new KPIs that measure agent success.

Gautheron suggests tracking:

• AI-driven traffic referrals: Monitor inbound visits from AI intermediaries and subsequent user behaviour using tools like Google Analytics.
• Share of Search: Extend beyond Google rankings to include visibility on AI Overviews and generative engines for natural-language queries.
• Share of Model: Measure how often your brand appears in outputs from large language models like GPT-4o, Gemini 2.0, or Llama 3.3, indicating relevance in AI-driven environments.

Marketers should assess AI interaction quality by tracking successful conversations, response relevance, and context retention. User sentiment matters too—how satisfied users are with personalised experiences and task completion. It’s also important to monitor how structured data like schema markup helps AI platforms find and deliver your content efficiently.

Understanding this feedback loop between users and AI reveals what works and where improvements are needed.

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