Agentic Ads: Preparing Your Brand for the AI-Mediated Web

Have you considered what happens when your next customer isn’t a person, but an AI agent? The commerce funnel is shifting dramatically. Today’s ads target humans with messages, visuals, and clickable experiences, but the rise of autonomous AI agents is upending this model. Increasingly, users delegate the entire shopping journey—searching, comparing, deciding, and purchasing—to these digital assistants.

This means traditional touchpoints like display ads, search rankings, product recommendations, and even your own website may be bypassed entirely.

As Meta’s CMO Alex Schultz puts it: “Ads are moving from targeting people to targeting agents”.

A major turning point is the case between Amazon and Perplexity AI. Amazon alleges Perplexity’s shopping agent, built into its “Comet” browser, accessed customer accounts, masked automated activity as human browsing, and placed orders on behalf of users—all without disclosure. Perplexity’s agent doesn’t refer shoppers back to Amazon—it selects and purchases on their behalf. If the agent becomes the trusted commerce layer, Amazon loses what matters most:

• Product search and discovery.
• Recommendation influence.
• Advertising visibility and spend.

Sir Tim Berners-Lee has warned, “If web pages are all read by LLMs, then people ask the LLM for the data and the LLM just produces the result, the whole ad-based business model of the web starts to fall apart”. 

Business leaders are beginning to recognize the stakes. John Bruce, CEO of Inrupt, notes that big brands and payment processors are “at an existential point of time” as they “potentially become subordinate to an LLM”.

For brands, this is the existential question:

When AI agents shop for your consumers, will your products even be considered?

Core Components for Brands to Become Agent-Ready

To succeed in this new landscape, brands must adapt their digital operations for agentic commerce. This requires a foundational shift from optimizing for human attention to providing structured, machine-readable information that AI can trust and act upon. Here are the core components every business should prioritize to become “agent-ready.”

Build a structured, agent-friendly product catalog

AI agents do not browse websites visually; they parse underlying data to evaluate products based on user-defined goals. A simple web page is insufficient. Your product catalog must be exposed as a rich, structured, and queryable data source. This means meticulously defining every product attribute—from technical specifications, materials, and dimensions to real-time pricing, stock availability, and shipping logistics.

To make this data intelligible to machines, it must be marked up with machine-readable schemas, such as those from Schema.org. By leveraging a Knowledge Graph, brands can go a step further, modeling not just product attributes but also the complex relationships between products, categories, and the brand itself. This creates a semantic backbone that allows an agent to understand, for instance, that a specific camera is compatible with a particular lens or that a certain laptop model is ideal for graphic design, ensuring your products are considered for relevant queries.

Enable agent-compatible checkout integration

In an agent-mediated transaction, the final “click to buy” is executed by the AI, not the human. Your e-commerce infrastructure must be prepared to handle these automated purchases securely and seamlessly. This requires supporting emerging standards like the Agentic Commerce Protocol (ACP), which provides a standardized way for agents to interact with checkout systems.

Crucially, this integration allows brands to retain control over the most critical parts of the transaction. While the AI agent acts as the user’s purchasing interface, the brand continues to manage payment processing, order fulfillment, returns, and—most importantly—the direct relationship with the customer. This ensures that even as the discovery funnel changes, the core commerce functions and customer data remain with your business.

Develop agent-aware advertising and sponsored slots

The metrics for advertising success are set to change entirely. Clicks and impressions, the cornerstones of traditional digital advertising, become irrelevant when the “viewer” is an AI. Agent-aware advertising models will instead measure success based on task completion and relevance to the agent’s evaluation criteria.

Sponsored placements will still exist, but they must be presented as transparent, data-rich offers that an agent can logically assess against a user’s needs. For example, an ad might target an agent shopping for a user who has specified a maximum budget, a desired delivery date, and a preference for sustainable materials. Success will depend on tracking conversions from these agent-driven flows and aligning sponsored offers with the explicit, data-driven needs evaluated by the agent.

Ensure governance, transparency, and user control

Trust is the currency of the agentic web. For users to delegate purchasing power to an AI, they must be confident that the agent is acting in their best interest. This necessitates a strong framework for governance and transparency. Agents must clearly disclose when a recommendation is sponsored or influenced by an advertisement.

Furthermore, the entire system must be auditable, allowing users and brands to understand why a particular choice was made. Users will also demand granular control over their agent’s preferences, including the ability to block certain brands or configure how the agent weighs factors like price versus quality. Brands that embrace these principles of transparency and user control will build the trust necessary to become a preferred choice for AI-mediated commerce.

Immediate Action Checklist

For brands ready to take practical steps, here are immediate actions to prepare for agent-driven commerce in the next 90 days:

1. Audit your catalog: Ensure product metadata, availability, SKUs, price feed are agent-ready.
2. Map your catalog onto your Knowledge Graph via WordLift: connect product-entities, brand, categories, attributes.
3. Engage with your commerce platform/PSP: Are you able to expose ACP-compatible endpoints? If not, plan for adaptation.
4. Define your “agent-ad” strategy: Which criteria will your brand target (price threshold, user profile, product specs)? Set budget/test model for agent flows.
5. Build analytics for “agent-initiated conversion”: Track which purchases came via agent checkout vs traditional funnel.
6. Define transparency practices: How will you label sponsored options in agent flows? How will agent identity/disclosure be surfaced?
7. Monitor platform opportunities: For example, apply with ChatGPT’s merchant program. (ChatGPT)

Ready to see how agent-friendly your website really is? Run our Agentic AI Audit today to discover how AI agents interpret your content and unlock new opportunities for visibility and conversion.

WordLift’s Role in the Agentic Web

Becoming agent-ready requires more than technology—it requires structured, high-quality product data and semantic enrichment. WordLift helps brands take full advantage of the agentic web:

• Semantic backbone for AI reasoning: WordLift’s Knowledge Graph + Schema-based modeling ensures product entities, attributes, and relationships are machine-readable and actionable for AI agents.
• AI Audit: Evaluates machine-readability, schema compliance, structured data quality, and internal linking. Beyond SEO, this audit ensures product data can reliably feed agent-enabled commerce flows.
• Agent-ad and checkout strategy integration: For enterprise clients, WordLift positions agent-aware advertising and checkout readiness within a broader agentic web visibility strategy.
• Early-mover advantage: Brands using WordLift can maintain visibility and influence in AI-mediated shopping flows, even as traditional ad-based funnels evolve.

Strategic Imperative

Winning in the agentic web will require brands to optimize for AI agents, not humans. High-quality, structured product data, agent-aware advertising, and seamless agent-enabled checkout will be key differentiators. The Amazon-Perplexity case is an early warning: brands that delay risk exclusion from AI-mediated commerce ecosystems, while those that act now can establish leadership in the agentic web.

Frequently Asked Questions

What are Agentic Ads?

Agentic Ads are advertisements designed to be understood and evaluated by AI agents, not humans. Instead of using visuals and slogans to persuade a person, they use structured, machine-readable data to prove a product’s relevance to an agent’s search criteria (like price, features, and delivery speed). The goal is to convince the AI agent to select and purchase your product on behalf of its user.

How do Agentic Ads differ from traditional advertisements?

The key difference is the audience. Traditional ads target humans, using creative content (images, video, persuasive copy) to appeal to emotion and influence a purchase decision. Agentic Ads target AI agents, using structured data (like product schemas, APIs, and knowledge graphs) to provide verifiable facts. The goal shifts from persuading a person to satisfying an agent’s logical, data-driven evaluation.

Why is structured data important for Agentic Ads?

Structured data is essential because it is the language that AI agents understand. An agent cannot “see” a webpage or “read” a clever marketing slogan; it parses data to compare products based on facts. Without structured data, your products are effectively invisible and will be excluded from the agent’s search and comparison process. It is the foundational requirement for your brand to even be considered in an AI-mediated purchase.