AI Agents Are Making Search Decisions for You – Is Your Site Ready?

For those already adjusting to generative engine optimization (GEO), the next shift has arrived. Agentic AI systems are beginning to shape search visibility in ways that go beyond ranking and snippets. These systems don’t just summarize – they carry out tasks. For SEO teams, that means content must be built not only for readers or algorithms, but for machines making decisions on behalf of users.

From generative to agentic systems

Generative AI reshaped search by compressing multiple links into single-page answers. GEO emerged in response, offering frameworks for structuring information clearly enough for large language models to reference. Agentic AI systems introduce a new model entirely. They interpret goals, select tools, and execute actions without constant user input.

An agent can analyze a request, access third-party platforms, check calendars, compare products, or send notifications. In some cases, the system decides how to complete a task without supervision. This shift changes how online experiences are constructed. It also redefines what optimization means.

How agentic workflows operate

Agentic systems follow task-based workflows. Consider a user looking for a waterproof jacket under $100. The agent can retrieve weather data, access user preferences, check retailer stock feeds, compare delivery windows, and finalize a purchase. These actions are performed across multiple sources with no new prompts from the user.

This model is already in development. Google's Search Generative Experience and Amazon's Alexa integrations reflect this direction. OpenAI has also announced memory and goal-driven features in ChatGPT that enable agents to manage context across sessions. As these systems are deployed, the technical requirements for content visibility change.

Structural changes to SEO

Agentic systems don’t rely on traditional index crawling alone. They require:

• Structured data in machine-readable formats, particularly schema markup

• Clean product feeds integrated into marketplaces or APIs

• Site elements that respond predictably to filters and user inputs

This means SEO teams must consider both ranking criteria and the technical accessibility of site content. If a system cannot retrieve real-time stock or delivery estimates, it may exclude that product from its decision-making workflow.

Content must support machine-led decisions

Marketing to agentic systems involves reshaping how information is delivered. * - 

• Snippets must answer discrete queries.

• Metadata needs to represent live status.

• Interfaces should not trap agents in endless filters or modals.

• Attribution will become harder to trace, since the user may not click through at all.

Research from Search Engine Journal (April 2025) found that AI agents selected content based on structured context and task relevance over traditional keyword matching. Text-based ads that clearly supported decision-making outperformed visual ads when agents were interpreting them. This will affect everything from PPC copy to how landing pages are structured.

Case study: Workflow automation in SEO

At Weights & Biases, a custom agentic workflow was deployed to assist editorial teams. The system collects target search terms, analyzes SERPs, extracts entities using Google’s NLP API, and produces article outlines. These outlines reflect author preferences by referencing past documents. The process reduces manual effort while improving output alignment. Monitoring is handled through W&B Weave to support iteration.

Actions SEO teams should prioritize

Start with an audit of how your site functions under automated user behavior. Agents must be able to complete key journeys, and that often means reworking JavaScript-heavy interfaces. Structured data should be implemented wherever relevant – especially on products, FAQs, and reviews.

Connecting feeds through services like Google Merchant Center or following emerging standards such as Anthropic’s Model Context Protocol makes your data more accessible to agents. Attribution models should also evolve. A sale initiated by an agent might be missing from click-through analytics. Brands need systems that can reconcile influence without direct traffic.

Internal experimentation helps build practical understanding. Tools like obot.ai make it possible to test basic agents against real-world workflows. These projects surface problems early and support organizational readiness.

Agentic AI is already shaping how information is found, ranked, and acted upon. SEO professionals who treat this as a technical problem to be solved – rather than a content problem to be explained – will be better positioned. Agents do not reward flashy headlines or polished brand narratives. They respond to clear logic, structured content, and systems built for execution.