Agent Experience (AX) in Content Operations: Helping AI Agents Succeed

AI agents are here to assist with content operations, so help them help you.

That’s the basic premise of what’s being called agent experience (AX). It builds upon the principles in user experience (UX) design, which turned technology from a set of non-comprehensive machines into indispensable tools we use in everyday life.

That little dopamine hit you get when you refresh a social media app and know new stories are about to be displayed? That’s UX design.

The satisfaction of seeing your personal details pre-populate in an online form so you can finish it more quickly? That’s UX design too.

UX design is sometimes confused or conflated with interface design or interaction design. Instead, it’s a holistic approach to understanding human wants and needs, so the technology is easy and even enjoyable to use.

AI agents don’t experience pleasure, but the more your content operations can support the work they do, the better they’ll be able to serve you. That includes both employees who create and manage enterprise marketing content and the audiences who consume it.

As you learn more about AX, never forget that everything you do is to enrich people’s lives. Designing for AI agents is simply a new step in that journey.  

What ‘agent experience’ really means

You were probably working on AX (or at least thinking about how to) before the term existed.

First coined by Matt Biilman in early 2025, AX recognizes that human beings are no longer directly handling all the tasks in creating content, searching for it, or managing applications that publish and distribute it.

For example, traditional UX was based on a reactive series of people tapping a screen or keys to tell an application to perform an action. The goal was to reduce the time spent figuring out where to navigate and the number of clicks required to achieve a desired result. The actions you take might be repeatable, but they’re static, screen-based, and dependent on visual clarity.

AX goes one step further because AI agents aren’t simply a set of robots mimicking how people engage with technology. Instead of a customer typing product terms into a search engine as they research a purchase, they can use a conversational AI interface that scans the public internet for appropriate results and summarizes them.

Answer engine optimization (AEO) techniques have been developing in response, as brands want to make sure generative AI tools can easily discover content and cite it.

In the same way, AX ensures AI agents aren’t struggling to sort through fragmented, scattered, or irrelevant content.

Instead, large language models (LLMs) can be trained on structured CMS data that makes it easier for AI agents to:

• Dig up relevant content assets to repurpose in a marketing campaign.
• Look up brand guidelines to develop metadata.
• Determine the best tags based on an established taxonomy.
• Alert employees when content needs additional reviews or should be removed.
• Check for compliance gaps or broken links.

Remember that the term agentic AI refers to technology that has the “agency” to perform work on an employee’s behalf. This can happen with human oversight or completely autonomously, but in both cases, AI agents will work better if they easily retrieve, interpret, or repurpose content on their own.

AX vs. UX: Both experiences matter

Just as enterprises still need to use search engine optimization (SEO) techniques but are adding AEO optimization, AX should be treated as an extension of your UX design goals.

In UX, a great site design will include a layout that guides people to the information or features they want. Those interfaces and interactions will remain critical as agentic AI is more widely adopted, because people will still be managing or approving AI agents and the results of their work through dashboards and other applications.

AX is like effectively onboarding AI agents to work with your team. Just as you wouldn’t want a new hire wasting hours searching in a storage closet for a file or trying to recreate a process that already exists, AX makes AI agents perform with greater accuracy and reliability. 

Employees will spend less time double-checking outputs for explainability and tweaking AI agents to avoid errors and duplication of work.

Here’s how to distinguish the core principles of UX and AX to assess how your content operations will need to evolve:

	UX	AX
Primary focus	Human users completing tasks smoothly	AI agents processing content accurately
Design basis	Research on human behavior to avoid assumptions	Content structured for machine readability
Information structure	Clear layouts, buttons, links, and visual hierarchy	Schema markup, content models, and taxonomies aligned to AI processing
Decision support	Simple, repeatable choices to reduce cognitive load	Logging and auditability to track prior decisions
Reliability	Consistent patterns, colors, and steps	Version control to improve trust and accuracy
Error recovery	Flexibility to reverse actions or go back a step	Clear workflows with human-in-the-loop rules

WordPress VIP supports AX through its content modeling, taxonomies, smart tagging, and workflows that define review and approval processes.

How the AI agent experience affects content publishing

Just like UX design, AX should be considered when building both internal and external experiences.

1. AX for employee-facing AI use

Agentic AI is already bringing a new set of core capabilities to content experience platforms (CXPs) like WordPress VIP. You can use AI agents like the WordPress AI Assistant to help draft content, design landing pages, and translate content, among other tasks.

Choosing an enterprise-grade platform gives you a huge head start on AX because much is already built in, including content authoring and management tools to automate metadata creation and structured fields that AI agents will use. 

The WordPress VIP dashboard centralizes access to sites, domains, and authentication, ensuring AI agents operate in a well-governed fashion.

They may not formally thank you, but AI agents will also benefit from the ability to organize a media library with custom taxonomies or integrate it with your own Digital Asset Management (DAM) solution. 

This all means AI agents will be able to help employees without frustrating mistakes, allowing people to get more done and tackle the most important issues.

2. AX for customer-facing AI use

Customers are increasingly expecting to see chatbots or intelligent virtual assistants (IVAs) greet them on a brand’s site. These AI-powered experiences help answer initial questions and provide direct support to address customer needs. 

In most cases, these experiences rely on AI agents being able to quickly draw upon relevant content, some of which may be published on your site and others on internal sites.

This is another area where creating machine-readable copy is crucial, as is providing context by explicitly defining relationships between content elements. WordPress VIP’s Content Intelligence lets you do this through Smart Tags, RAG-style retrieval, automated excerpt generation, and the Content API that recommends relevant content for specific queries.

Meanwhile, Open APIs like REST and GraphQL, along with emerging standards like Model Context Protocol (MCP), let you connect third-party AI agents to your website. This means you can put more AI agents to work without having to worry about their ability to “fit in” to your current environment.

Getting started with AX in content operations

It took years for most large enterprises to fully appreciate and implement UX design best practices, and it’s still a constant work in progress. AX is similar in that you need to lay the groundwork now, recognizing that it takes ongoing learning and operational discipline to ensure AI agents perform at their best.

Begin by connecting with your UX or web admin team to see what they’re already doing to optimize for AI agents. Make sure other relevant stakeholders understand what AX is and why it’s important as you invest more resources in it.

As your AX optimization efforts evolve, measure success (or tweak your approach) based on key performance indicators (KPIs) like improved search discoverability, internal AI agent accuracy, and AI-derived content performance.

Most importantly, keep asking yourself: How is the AX work we’re doing helping our audience? Even if you’re offering AI agents a great experience, it’s people who will (and should) ultimately feel the impact.