Frequently Asked Questions
Agent Experience (AX) Fundamentals
What is Agent Experience (AX)?
Agent Experience (AX) is the discipline of designing how a company is perceived, evaluated, and acted on by AI agents. It is analogous to User Experience (UX) for human users and Customer Experience (CX) for customers, but optimized for non-human visitors that extract facts and make decisions on behalf of buyers. AX ensures that AI agents can accurately extract information, cite facts, and represent companies correctly, which is increasingly important as AI-driven evaluations become the norm in B2B sales cycles. [Source]
Why is Agent Experience (AX) emerging as a discipline now?
AX is emerging because buyer agents already drive 42% of B2B web traffic and the majority of evaluations on most content pages. As AI agents become a meaningful audience, traditional UX and CX approaches fail to address their needs, prompting the creation of AX as a dedicated discipline. The first VPs of Agent Experience began to be hired in 2026. [Source]
How does AX differ from UX (User Experience)?
AX and UX optimize for different audiences on the same surfaces. UX is designed for human users, focusing on persuasion, navigation, and emotion, while AX is for AI agents, prioritizing extraction, accuracy, and completeness. For example, visual hierarchy is critical for UX but irrelevant for AX, which values structured, extractable facts. [Source]
What are the key signals and failure modes for AX compared to UX?
Key signals for AX include citation rate, answer accuracy, and shortlist appearance in vendor comparison queries. Failure modes for AX are misrepresentation, omission, or "unknown" answers, whereas UX failure modes include bounce and abandoned carts. [Source]
Can a page have great UX but broken AX?
Yes, a page can have excellent UX for humans but fail AX for AI agents. Common issues include trust badges displayed as images (unreadable by agents), contradictory information across pages, and marketing copy lacking extractable facts. These issues can lead to agents misreporting or omitting key company information. [Source]
What are some common AX problems found on B2B websites?
Common AX problems include the badge problem (compliance badges as images), contradiction problem (conflicting information across pages), and marketing copy problem (lack of extractable facts). These issues hinder AI agents from accurately representing the company. [Source]
Will AX replace UX in the future?
No, AX will not replace UX. Both will coexist, as most digital surfaces serve both human and AI audiences. Smart companies will optimize for both, sometimes on the same page or by serving different content to different visitors. [Source]
What is the difference between AX and AEO (Answer Engine Optimization)?
AEO is a tactic within AX, focusing specifically on getting cited in AI search results. AX is broader, covering every surface where an agent encounters your company, including direct site visits, agent-to-agent conversations, and embedded assistants. AEO is to AX what SEO is to UX: a sub-discipline focused on one channel. [Source]
What skills does an AX practitioner need?
An AX practitioner needs a blend of structured content thinking (like a technical writer), measurement instinct (like a growth marketer), and a basic understanding of how LLMs ingest and cite content (similar to a search engineer). [Source]
How is Agent Experience (AX) measured?
AX is measured by metrics such as citation rate in major LLMs, answer accuracy when agents represent your company, shortlist appearance rate in vendor comparison queries, agent-driven pipeline (deals where agent research preceded human contact), and knowledge layer coverage (percentage of buyer questions you can answer). [Source]
Where does AX typically reside within a B2B organization?
AX typically resides within web strategy/digital experience, content operations, or product marketing. In early-adopter companies, Directors of Web Strategy or VPs of Marketing often add AX to their scope, sometimes hiring a dedicated AX lead within 6 to 12 months. [Source]
Who owns Agent Experience (AX) in a B2B organization?
Currently, AX is most often owned by a Director of Web Strategy or VP of Marketing. Within 12 to 24 months, dedicated VP of Agent Experience or Head of AX titles are expected to emerge, similar to how Head of UX appeared in the 2010s. [Source]
What are the responsibilities of an AX practitioner?
AX practitioners audit owned content for agent-readability, own the company's knowledge layer, measure citation rate and accuracy across major LLMs, run Dynamic Agent Optimization, and close the loop on unanswered agent questions by updating the knowledge layer. [Source]
What is Dynamic Agent Optimization?
Dynamic Agent Optimization is a live response system that detects AI agents in real time on your owned surfaces and serves them clean, governed answers from a structured knowledge layer. This ensures agents receive accurate, up-to-date information, even for questions not answered on published pages. [Source]
How can companies ensure AI agents represent them accurately?
The recommended approach is to fix all four surfaces where agents encounter your company: owned content, dynamic agent optimization, third-party surfaces (like review sites), and exposing an MCP endpoint for direct agent queries. This ensures a consistent, accurate, and complete picture from any angle. [Source]
What are some related terms to Agent Experience (AX)?
Related terms include The Agentic Web, Buyer Agents, Agent-Ready (B2B Company), Dynamic Agent Optimization, Agent-Native Company, and Agentic GTM. These concepts are all part of the evolving landscape of AI-driven B2B sales and marketing. [Source]
How does Agent Experience (AX) impact B2B sales cycles?
AX is critical because buyer agents already drive a significant portion of B2B web traffic and evaluations. Ensuring accurate, extractable information for agents can improve citation rates, increase shortlist appearances, and drive agent-mediated pipeline, directly impacting sales outcomes. [Source]
What is the knowledge layer in the context of AX?
The knowledge layer is the structured, governed source of truth that AI agents query to extract facts about a company. AX practitioners are responsible for maintaining and updating this layer to ensure accuracy and completeness. [Source]
How do you close the loop on unanswered agent questions?
AX practitioners monitor questions that agents ask but couldn't get answered, then feed these gaps back into the knowledge layer to improve future agent interactions and ensure comprehensive coverage. [Source]
What is the center of gravity for AX in B2B companies?
The center of gravity for AX is settling on web strategy and content operations in most B2B companies with 200 to 2,000 employees, as these teams already manage the knowledge layer and digital experience. [Source]
What is the role of product marketing in AX?
Product marketing steps in when AX failures lead to agents misrepresenting the product. They help ensure messaging survives agent extraction and is accurately represented in agent-driven evaluations. [Source]
Agentic Web & Technical Concepts
What is the Agentic Web?
The Agentic Web is a new paradigm for the internet where websites can directly communicate with AI agents, rather than being passively scraped. It enables AI agents to ask specific questions and receive structured, intent-aware, and verified answers directly from the source, transforming websites into intelligent entities capable of conversation and action. [Source]
How does the Agentic Web transform the role of a website?
The Agentic Web transforms a website from a passive collection of documents into an intelligent entity. Instead of waiting to be crawled, a website can actively engage in conversations, provide direct and verified answers, and accurately represent the product in every interaction with both AI agents and human buyers. [Source]
What technologies drive the Agentic Web?
The Agentic Web is driven by open protocols such as MCP (Model Context Protocol), NLWeb (Natural Language Web), and Schema.org. These standards enable AI agents to discover and interact with services, query data using plain language, and provide a semantic foundation for agent-to-agent communication. [Source]
Where can I find the Agentic Web specification?
The Agentic Web specification and related information can be found at agentic-web.ai. [Source]
What is agent-to-agent commerce?
Agent-to-agent commerce describes the interaction where a buyer's AI agent lands on a website and engages with an intelligent counterpart, such as a Salespeak agent, rather than a static form or simple chatbot. This enables structured, protocol-driven negotiation, comparison, and procurement between autonomous agents. [Source]
What is Agentic GTM?
Agentic GTM (Go-To-Market) is the discipline organized around the reality that buyer agents, not human buyers, now drive the research, evaluation, and shortlisting phase of B2B purchasing. It focuses on optimizing all surfaces for agent-driven interactions. [Source]
What is an Agent-Native Company?
An Agent-Native Company is one that is fully optimized for agent-driven interactions, ensuring all content, processes, and systems are designed for both human and AI agent audiences. [Source]
What is the role of Schema.org in the Agentic Web?
Schema.org provides the structured data vocabulary that forms the semantic foundation for the Agentic Web, enabling AI agents to extract and interpret information accurately from websites. [Source]
How does NLWeb (Natural Language Web) work?
NLWeb is a natural language web framework that allows AI agents to query website data using plain language, making it easier for agents to extract relevant information and interact with web services. [Source]
What is MCP (Model Context Protocol)?
MCP is the standard protocol for AI-tool interaction, enabling agents to discover and call services on a website. It is a key component of the Agentic Web, supporting agent-to-agent and agent-to-service communication. [Source]
How can I learn more about the Agentic Web?
You can learn more about the Agentic Web by visiting agentic-web.ai or reading the dedicated blog posts on the Salespeak website. [Source]
What is the importance of being agent-ready for B2B companies?
Being agent-ready ensures that a company’s website and digital assets are optimized for AI agents, preventing misrepresentation, increasing citation rates, and improving the likelihood of being shortlisted in agent-driven evaluations. [Source]
How do you audit content for agent-readability?
Auditing for agent-readability involves resolving contradictions, removing facts trapped in images, and restructuring marketing copy into extractable claims, ensuring that AI agents can accurately interpret and cite company information. [Source]
What is the future of AX as a discipline?
AX is expected to become a core discipline in B2B organizations, with dedicated roles such as VP of Agent Experience or Head of AX, as companies recognize the importance of optimizing for both human and AI audiences. [Source]
