Learning Path
Navigate the AXO curriculum
Fundamentals
Implementation
AXO Case Studies
Real-world examples of Agent Experience Optimization implementations with documented before/after comparisons and measured outcomes.
E-commerce Product Documentation
Mid-size electronics retailer optimizing product pages for AI agent discovery
+180% Agent Citations
March 2024 - May 2024
Before Optimization
Issues Identified:
- • No structured data markup
- • Generic product descriptions
- • Missing technical specifications
- • No clear pricing information
- • Poor heading structure
Sample Before HTML:
<div class="product"> <h2>Amazing Wireless Headphones</h2> <p>Experience incredible sound quality with our premium wireless headphones. Perfect for music lovers!</p> <div class="price">$199.99</div> </div>
After Optimization
Improvements Made:
- • Added Product schema markup
- • Detailed technical specifications
- • Clear pricing and availability
- • Proper heading hierarchy
- • FAQ section with common questions
Sample After HTML:
<article itemscope itemtype="https://schema.org/Product">
<h1 itemprop="name">Sony WH-1000XM4 Wireless Noise-Canceling Headphones</h1>
<div itemprop="description">
Professional-grade wireless headphones with industry-leading noise cancellation technology. 30-hour battery life, Hi-Res Audio support.
</div>
<div itemprop="offers" itemscope itemtype="https://schema.org/Offer">
<span itemprop="price">199.99</span>
<span itemprop="priceCurrency">USD</span>
</div>
</article>Measured Results (8 weeks)
+180%
AI Agent Citations
+45%
Organic Traffic
+23%
Conversion Rate
API Documentation Optimization
SaaS platform improving developer documentation for AI agent consumption
+240% Developer Queries
January 2024 - March 2024
Before Optimization
Documentation Issues:
- • Scattered endpoint information
- • Inconsistent example formats
- • Missing error code documentation
- • No machine-readable API spec
- • Vague parameter descriptions
Before: Vague Documentation
## User API Get user information by sending a request to our user endpoint. Returns user data in JSON format. Example: GET /api/user
After Optimization
Improvements Made:
- • OpenAPI 3.0 specification
- • Detailed parameter descriptions
- • Complete example requests/responses
- • Error code documentation
- • Rate limiting information
After: Structured Documentation
## GET /api/v1/users/{userId}
Retrieves detailed information for a specific user account.
### Parameters
- userId (string, required): Unique user identifier (UUID format)
### Response (200 OK)
{
"id": "550e8400-e29b-41d4-a716-446655440000",
"email": "user@example.com",
"created_at": "2024-01-15T10:30:00Z"
}
### Error Codes
- 404: User not found
- 401: Authentication requiredMeasured Results (8 weeks)
+240%
AI Developer Queries
+67%
API Adoption Rate
-40%
Support Tickets
Educational Blog Transformation
Educational technology company optimizing tutorial content for AI agents
+156% Citation Rate
February 2024 - April 2024
Before: Marketing-Heavy Content
- • "Revolutionary new approach to learning"
- • "Game-changing methodology"
- • Vague benefit statements
- • No specific examples or data
- • Poor heading structure
After: Fact-Based Content
- • "Spaced repetition increases retention by 67%"
- • Step-by-step implementation guides
- • Specific research citations
- • Concrete examples with data
- • Clear H1-H6 hierarchy
Content Structure Transformation
Before Structure:
• Generic title: "Better Learning Methods"
• No clear sections
• Mixed heading levels
• No structured data
After Structure:
• Specific title: "Spaced Repetition: Implementation Guide"
• Clear H2 sections: Method, Benefits, Implementation
• Proper heading hierarchy
• Article schema with FAQ section
Measured Results (8 weeks)
+156%
AI Citations
+89%
Organic Traffic
+34%
Time on Page
+78%
Social Shares
Key Success Patterns
1
Structure First
Proper heading hierarchy and semantic markup showed the most immediate impact on agent discovery.
2
Specificity Wins
Replacing vague marketing language with specific facts and data dramatically improved citation rates.
3
Schema Matters
Structured data markup consistently improved agent understanding and proper attribution.