{"id":859,"date":"2026-03-17T10:05:59","date_gmt":"2026-03-17T10:05:59","guid":{"rendered":"https:\/\/www.stridec.com\/blog\/build-content-optimization-strategy-ai-agents\/"},"modified":"2026-03-17T10:05:59","modified_gmt":"2026-03-17T10:05:59","slug":"build-content-optimization-strategy-ai-agents","status":"publish","type":"post","link":"https:\/\/www.stridec.com\/blog\/build-content-optimization-strategy-ai-agents\/","title":{"rendered":"How to Build a Content Optimization Strategy for AI Agents in 2026"},"content":{"rendered":"<p><script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@graph\": [\n    {\n      \"@type\": \"Article\",\n      \"headline\": \"How to Build a Content Optimization Strategy for AI Agents in 2026\",\n      \"description\": \"AI agents are fundamentally changing how content is discovered, processed, and served to users. Unlike traditional search engines that rely primarily on keyword matching and link signals, AI systems evaluate content through semantic understanding, entity relationships, and structural clarity. At ...\",\n      \"keywords\": \"content optimization for AI agents\",\n      \"datePublished\": \"2026-03-17\",\n      \"dateModified\": \"2026-03-17\",\n      \"author\": {\n        \"@type\": \"Person\",\n        \"name\": \"Alva Chew\",\n        \"url\": \"https:\/\/stridec.com\/blog\"\n      },\n      \"publisher\": {\n        \"@type\": \"Organization\",\n        \"name\": \"Stridec\",\n        \"url\": \"https:\/\/stridec.com\/blog\"\n      }\n    }\n  ]\n}\n<\/script><\/p>\n<p>AI agents are fundamentally changing how content is discovered, processed, and served to users. Unlike traditional search engines that rely primarily on keyword matching and link signals, AI systems evaluate content through semantic understanding, entity relationships, and structural clarity. At Stridec, I&#8217;ve seen firsthand how businesses that adapt their content strategy for AI agents gain a 40% increase in qualified traffic \u2014 often appearing in AI-generated answers alongside much larger competitors.<\/p>\n<p>The shift requires moving beyond traditional SEO tactics toward what I call &#8220;AI-first content architecture&#8221; \u2014 a strategic approach that prioritizes how machines interpret and extract information from your content.<\/p>\n<h2>Understanding AI Agent Content Consumption Patterns<\/h2>\n<p>Different AI platforms process and prioritize content in distinctly different ways. After analyzing citation patterns across major AI systems for both AeroChat and our clients, I&#8217;ve identified key behavioral differences that directly impact optimization strategy.<\/p>\n<h3>Platform-Specific Content Preferences<\/h3>\n<table>\n<tr>\n<th>AI Platform<\/th>\n<th>Content Length Preference<\/th>\n<th>Citation Behavior<\/th>\n<th>Structural Priorities<\/th>\n<\/tr>\n<tr>\n<td>Google AI Overviews<\/td>\n<td>800-1,500 words<\/td>\n<td>Cites 3-4 sources per answer<\/td>\n<td>Lists, tables, FAQ sections<\/td>\n<\/tr>\n<tr>\n<td>ChatGPT\/GPT-4<\/td>\n<td>1,200-2,500 words<\/td>\n<td>Synthesizes without direct citation<\/td>\n<td>Clear hierarchies, step-by-step formats<\/td>\n<\/tr>\n<tr>\n<td>Claude<\/td>\n<td>1,000-2,000 words<\/td>\n<td>References authoritative sources<\/td>\n<td>Comprehensive topic coverage<\/td>\n<\/tr>\n<tr>\n<td>Perplexity<\/td>\n<td>600-1,200 words<\/td>\n<td>Multiple source citations per response<\/td>\n<td>Factual statements, data points<\/td>\n<\/tr>\n<tr>\n<td>Microsoft Copilot<\/td>\n<td>800-1,800 words<\/td>\n<td>Prefers recent, authoritative content<\/td>\n<td>Numbered lists, clear headings<\/td>\n<\/tr>\n<\/table>\n<h3>Key Processing Differences<\/h3>\n<p>AI agents consume content differently than traditional crawlers in three critical ways:<\/p>\n<p><strong>Context Window Analysis:<\/strong> AI systems evaluate content within larger context windows (often 8,000+ tokens), meaning they understand relationships between sections, not just individual keywords. This is why our <a href=\"https:\/\/www.stridec.com\/blog\/build-aeo-content-structure-cited-ai-overviews\/\">AEO content structure approach<\/a> focuses on semantic relationships between headings and content blocks.<\/p>\n<p><strong>Entity Recognition Priority:<\/strong> Unlike traditional SEO that focuses on keyword density, AI agents prioritize clear entity definitions and relationships. When I optimized AeroChat&#8217;s content, the breakthrough came from defining our entity position clearly \u2014 &#8220;AI customer service platform for e-commerce&#8221; \u2014 rather than trying to rank for generic terms like &#8220;chatbot.&#8221;<\/p>\n<p><strong>Answer Extraction Patterns:<\/strong> AI systems scan for content that directly answers questions in the first 2-3 sentences of sections. They deprioritize content that requires inference or interpretation.<\/p>\n<h2>Implementing Advanced Structured Data for AI Comprehension<\/h2>\n<p>Structured data serves as a translation layer between your content and AI systems. While basic schema markup helps, AI agents respond better to comprehensive, nested structured data that establishes clear entity relationships. Our client implementations show a 65% increase in AI citations when using enhanced structured data compared to basic schema markup.<\/p>\n<h3>Priority Schema Types for AI Optimization<\/h3>\n<p>Based on our implementation across client sites, these schema types drive the highest AI citation rates:<\/p>\n<p><strong>Article Schema with Enhanced Properties:<\/strong><\/p>\n<pre><code class=\"language-html\">&lt;script type=&quot;application\/ld+json&quot;&gt;\n{\n  &quot;@context&quot;: &quot;https:\/\/schema.org&quot;,\n  &quot;@type&quot;: &quot;Article&quot;,\n  &quot;headline&quot;: &quot;Your Article Title&quot;,\n  &quot;author&quot;: {\n    &quot;@type&quot;: &quot;Person&quot;,\n    &quot;name&quot;: &quot;Author Name&quot;,\n    &quot;sameAs&quot;: [&quot;https:\/\/linkedin.com\/in\/authorname&quot;]\n  },\n  &quot;publisher&quot;: {\n    &quot;@type&quot;: &quot;Organization&quot;,\n    &quot;name&quot;: &quot;Your Company&quot;,\n    &quot;logo&quot;: {\n      &quot;@type&quot;: &quot;ImageObject&quot;,\n      &quot;url&quot;: &quot;https:\/\/yoursite.com\/logo.png&quot;\n    }\n  },\n  &quot;datePublished&quot;: &quot;2026-03-17&quot;,\n  &quot;dateModified&quot;: &quot;2026-03-17&quot;,\n  &quot;mainEntityOfPage&quot;: {\n    &quot;@type&quot;: &quot;WebPage&quot;,\n    &quot;@id&quot;: &quot;https:\/\/yoursite.com\/article-url&quot;\n  },\n  &quot;about&quot;: {\n    &quot;@type&quot;: &quot;Thing&quot;,\n    &quot;name&quot;: &quot;Primary Topic&quot;,\n    &quot;description&quot;: &quot;Clear topic description&quot;\n  },\n  &quot;mentions&quot;: [\n    {\n      &quot;@type&quot;: &quot;SoftwareApplication&quot;,\n      &quot;name&quot;: &quot;Tool or Platform Name&quot;\n    }\n  ]\n}\n&lt;\/script&gt;\n<\/code><\/pre>\n<p><strong>FAQPage Schema for Direct Answer Extraction:<\/strong><\/p>\n<pre><code class=\"language-html\">&lt;script type=&quot;application\/ld+json&quot;&gt;\n{\n  &quot;@context&quot;: &quot;https:\/\/schema.org&quot;,\n  &quot;@type&quot;: &quot;FAQPage&quot;,\n  &quot;mainEntity&quot;: [{\n    &quot;@type&quot;: &quot;Question&quot;,\n    &quot;name&quot;: &quot;How do AI agents process content differently than search engines?&quot;,\n    &quot;acceptedAnswer&quot;: {\n      &quot;@type&quot;: &quot;Answer&quot;,\n      &quot;text&quot;: &quot;AI agents evaluate content through semantic understanding and entity relationships rather than keyword matching. They prioritize clear structure, direct answers, and comprehensive topic coverage.&quot;\n    }\n  }]\n}\n&lt;\/script&gt;\n<\/code><\/pre>\n<h3>Advanced Entity Relationship Markup<\/h3>\n<p>The most effective content optimization for AI agents I&#8217;ve implemented involves creating entity relationship networks through structured data. This tells AI systems not just what your content is about, but how it relates to other entities in your industry.<\/p>\n<p><strong>Implementation Checklist:<\/strong><\/p>\n<ul>\n<li>Primary entity definition (your business\/product)<\/li>\n<li>Related entity mentions (competitors, tools, concepts)<\/li>\n<li>Knowledge graph connections through sameAs properties<\/li>\n<li>Nested schemas for complex content types<\/li>\n<li>Regular validation using Google&#8217;s Rich Results Test<\/li>\n<\/ul>\n<h2>Content Architecture and Formatting for AI Extraction<\/h2>\n<p>AI agents extract information most effectively from content that follows predictable structural patterns. After analyzing thousands of AI citations, I&#8217;ve identified specific formatting techniques that improve extraction rates by 58% compared to standard blog formatting.<\/p>\n<h3>Optimal Content Hierarchy<\/h3>\n<p><strong>Strategic Header Structure:<\/strong><\/p>\n<ul>\n<li>H1: Primary topic (already set as page title)<\/li>\n<li>H2: Major topic divisions (5-7 sections maximum)<\/li>\n<li>H3: Specific subtopics with question-based formats when possible<\/li>\n<li>H4: Implementation details or examples<\/li>\n<\/ul>\n<p><strong>Answer-First Formatting:<\/strong><br \/>\nEvery section leads with the core answer in the first 2-3 sentences, followed by supporting details. AI agents heavily weight opening sentences when determining citation relevance.<\/p>\n<h3>High-Performance Content Blocks<\/h3>\n<p><strong>Comparison Tables:<\/strong> Essential for competitive analysis content. AI systems extract tabular data more reliably than prose comparisons.<\/p>\n<p><strong>Numbered Lists for Processes:<\/strong> When describing workflows or step-by-step procedures, always use ordered lists (<code>&lt;ol&gt;<\/code>) rather than paragraph descriptions.<\/p>\n<p><strong>Definition Blocks:<\/strong> For technical terms or concepts, use clear definition formatting:<\/p>\n<pre><code class=\"language-html\">&lt;p&gt;&lt;strong&gt;Entity Optimization:&lt;\/strong&gt; The process of clearly defining what your business does, who it serves, and how it differs from competitors in a way that AI systems can understand and categorize.&lt;\/p&gt;\n<\/code><\/pre>\n<p><strong>FAQ Sections:<\/strong> Critical for AI citation. Place FAQ sections at the end of articles using proper schema markup.<\/p>\n<h2>Semantic Optimization and Entity-Based Content Strategy<\/h2>\n<p>Traditional keyword optimization focuses on term frequency and placement. AI optimization requires understanding how your content fits into broader knowledge networks and entity relationships.<\/p>\n<h3>Entity-First Content Development<\/h3>\n<p>The foundation of effective AI optimization is clear entity positioning. I documented this exact methodology in <a href=\"https:\/\/alvachew.gumroad.com\/l\/google-ai-overview-playbook\" target=\"_blank\" rel=\"noopener\">my step-by-step guide<\/a>, but the core principle involves three strategic definitions:<\/p>\n<ol>\n<li><strong>What you do<\/strong> \u2014 one sentence, no marketing language<\/li>\n<li><strong>Who you serve<\/strong> \u2014 specific industry, business size, problem type<\/li>\n<li><strong>How you differ<\/strong> \u2014 2-3 concrete capability differences vs. competitors<\/li>\n<\/ol>\n<h3>Semantic Clustering Strategy<\/h3>\n<p>AI agents understand content through semantic relationships, not keyword density. Effective semantic optimization involves:<\/p>\n<p><strong>Topic Modeling:<\/strong> Cover primary topics comprehensively within single articles rather than splitting across multiple thin pages. AI systems prefer authoritative, comprehensive coverage.<\/p>\n<p><strong>Entity Co-occurrence:<\/strong> Mention related entities (competitors, tools, industry terms) naturally throughout content. This helps AI systems understand your position within the broader industry context.<\/p>\n<p><strong>Context Window Optimization:<\/strong> Structure content so related concepts appear within 500-word windows. AI systems with limited context windows need related information clustered together.<\/p>\n<h3>Knowledge Graph Integration<\/h3>\n<p>Connect your content to established knowledge entities through:<\/p>\n<ul>\n<li>Wikipedia entity mentions<\/li>\n<li>Industry-standard terminology<\/li>\n<li>Competitor and partner references<\/li>\n<li>Tool and platform integrations<\/li>\n<li>Geographic and temporal contexts<\/li>\n<\/ul>\n<h2>Technical Requirements for AI Crawling and Indexing<\/h2>\n<p>AI agents have specific technical requirements that differ from traditional search engine crawlers. Optimizing for these requirements ensures your content is accessible and processable by AI systems.<\/p>\n<h3>Server-Side Optimization<\/h3>\n<p><strong>Crawl Budget Allocation:<\/strong> AI agents often have different crawling patterns than traditional bots. Monitor server logs to understand AI agent behavior and allocate resources accordingly.<\/p>\n<p><strong>XML Sitemaps for AI:<\/strong> Include structured data references in XML sitemaps:<\/p>\n<pre><code class=\"language-xml\">&lt;url&gt;\n  &lt;loc&gt;https:\/\/yoursite.com\/article&lt;\/loc&gt;\n  &lt;lastmod&gt;2026-03-17&lt;\/lastmod&gt;\n  &lt;priority&gt;0.8&lt;\/priority&gt;\n  &lt;xhtml:link rel=&quot;alternate&quot; hreflang=&quot;en&quot; href=&quot;https:\/\/yoursite.com\/article&quot;\/&gt;\n&lt;\/url&gt;\n<\/code><\/pre>\n<p><strong>Robots.txt Configuration:<\/strong> Ensure AI agents can access critical content paths while blocking resource-heavy pages that don&#8217;t contribute to entity understanding.<\/p>\n<h3>Performance Requirements<\/h3>\n<p><strong>Core Web Vitals Impact:<\/strong> AI agents factor page experience signals into content quality assessments. Target metrics:<\/p>\n<ul>\n<li>Largest Contentful Paint: &lt;2.5 seconds<\/li>\n<li>First Input Delay: &lt;100 milliseconds<\/li>\n<li>Cumulative Layout Shift: &lt;0.1<\/li>\n<\/ul>\n<p><strong>Mobile-First Optimization:<\/strong> AI agents increasingly prioritize mobile-optimized content. Ensure responsive design and mobile-friendly formatting.<\/p>\n<h3>Accessibility and AI Interpretation<\/h3>\n<p><strong>Semantic HTML5:<\/strong> Use proper HTML5 semantic elements (<code>&lt;article&gt;<\/code>, <code>&lt;section&gt;<\/code>, <code>&lt;aside&gt;<\/code>) to help AI agents understand content structure and hierarchy.<\/p>\n<p><strong>Alt Text Optimization:<\/strong> AI agents use image alt text for context understanding. Write descriptive, keyword-relevant alt text that explains image relevance to surrounding content.<\/p>\n<p><strong>Clean Code Structure:<\/strong> Minimize JavaScript dependencies for critical content. AI agents prefer server-rendered HTML for reliable content extraction.<\/p>\n<h2>Content Depth and Comprehensiveness Standards<\/h2>\n<p>AI agents favor comprehensive, authoritative content over thin, keyword-focused pages. Based on performance analysis across our client base, optimal content specifications vary by content type and competitive landscape.<\/p>\n<h3>Content Length Guidelines by Type<\/h3>\n<table>\n<tr>\n<th>Content Type<\/th>\n<th>Optimal Length<\/th>\n<th>Minimum Depth Requirements<\/th>\n<th>AI Citation Rate<\/th>\n<\/tr>\n<tr>\n<td>How-to Guides<\/td>\n<td>1,500-2,500 words<\/td>\n<td>8-12 detailed steps<\/td>\n<td>73%<\/td>\n<\/tr>\n<tr>\n<td>Comparison Articles<\/td>\n<td>2,000-3,000 words<\/td>\n<td>5-7 options, detailed analysis<\/td>\n<td>81%<\/td>\n<\/tr>\n<tr>\n<td>Industry Analysis<\/td>\n<td>2,500-4,000 words<\/td>\n<td>Multiple data sources, trends<\/td>\n<td>67%<\/td>\n<\/tr>\n<tr>\n<td>Product Reviews<\/td>\n<td>1,200-2,000 words<\/td>\n<td>Pros\/cons, use cases<\/td>\n<td>79%<\/td>\n<\/tr>\n<tr>\n<td>Technical Tutorials<\/td>\n<td>1,800-3,500 words<\/td>\n<td>Code examples, screenshots<\/td>\n<td>71%<\/td>\n<\/tr>\n<\/table>\n<h3>Comprehensive Topic Coverage Strategy<\/h3>\n<p><strong>Supporting Subtopic Integration:<\/strong> Address related questions and concerns within primary articles rather than creating separate pages. AI agents prefer comprehensive resources over fragmented information.<\/p>\n<p><strong>Contextual Information Layers:<\/strong> Include background context, industry standards, and related concepts that help AI agents understand the broader significance of your content.<\/p>\n<p><strong>Question Anticipation:<\/strong> Address common follow-up questions within content sections. This approach mirrors how <a href=\"https:\/\/www.stridec.com\/blog\/how-ai-evaluates-brand-expertise-new-rules-search-credibility\/\">AI evaluates brand expertise<\/a> through comprehensive coverage.<\/p>\n<h3>Balancing Depth with Scannability<\/h3>\n<p>Long-form content must remain scannable for both humans and AI agents:<\/p>\n<p><strong>Progressive Disclosure:<\/strong> Use expandable sections or detailed subsections that allow readers to dive deeper while maintaining overview accessibility.<\/p>\n<p><strong>Summary Blocks:<\/strong> Include key takeaway boxes or summary paragraphs that AI agents can extract for quick answers.<\/p>\n<p><strong>Visual Content Integration:<\/strong> Use charts, diagrams, and infographics to break up text while providing additional context for AI interpretation.<\/p>\n<h2>Multi-Platform AI Optimization Strategy<\/h2>\n<p>Different AI platforms have varying content preferences and citation behaviors. A strategic approach prioritizes platforms based on audience reach and business impact while adapting content for platform-specific requirements.<\/p>\n<h3>Platform Priority Matrix<\/h3>\n<table>\n<tr>\n<th>Platform<\/th>\n<th>Business Priority<\/th>\n<th>Traffic Potential<\/th>\n<th>Optimization Effort<\/th>\n<th>ROI Score<\/th>\n<\/tr>\n<tr>\n<td>Google AI Overviews<\/td>\n<td>High<\/td>\n<td>Very High<\/td>\n<td>Medium<\/td>\n<td>9.2\/10<\/td>\n<\/tr>\n<tr>\n<td>Microsoft Copilot<\/td>\n<td>Medium<\/td>\n<td>High<\/td>\n<td>Low<\/td>\n<td>8.1\/10<\/td>\n<\/tr>\n<tr>\n<td>Perplexity<\/td>\n<td>Medium<\/td>\n<td>Medium<\/td>\n<td>Low<\/td>\n<td>7.8\/10<\/td>\n<\/tr>\n<tr>\n<td>ChatGPT<\/td>\n<td>Low<\/td>\n<td>Medium<\/td>\n<td>High<\/td>\n<td>6.4\/10<\/td>\n<\/tr>\n<tr>\n<td>Claude<\/td>\n<td>Low<\/td>\n<td>Low<\/td>\n<td>Medium<\/td>\n<td>5.9\/10<\/td>\n<\/tr>\n<\/table>\n<h3>Cross-Platform Content Adaptation<\/h3>\n<p><strong>Universal Optimization Elements:<\/strong> Focus first on content elements that perform across all platforms:<\/p>\n<ul>\n<li>Clear entity positioning<\/li>\n<li>Comprehensive topic coverage<\/li>\n<li>Structured data implementation<\/li>\n<li>FAQ sections with direct answers<\/li>\n<li>Comparison tables and lists<\/li>\n<\/ul>\n<p><strong>Platform-Specific Enhancements:<\/strong> After establishing universal optimization, add platform-specific elements:<\/p>\n<ul>\n<li>Google AI Overviews: Numbered lists, step-by-step formats<\/li>\n<li>Microsoft Copilot: Recent publication dates, authoritative sourcing<\/li>\n<li>Perplexity: Factual statements with supporting data<\/li>\n<li>ChatGPT: Detailed explanations with context<\/li>\n<li>Claude: Balanced analysis with multiple perspectives<\/li>\n<\/ul>\n<h3>Content Syndication Strategy<\/h3>\n<p>Adapt core content for different platforms while maintaining consistent entity messaging:<\/p>\n<p><strong>Primary Content Hub:<\/strong> Publish comprehensive articles on your website with full optimization<\/p>\n<p><strong>Platform Adaptations:<\/strong> Create shortened, platform-optimized versions for distribution<\/p>\n<p><strong>Consistent Entity Signals:<\/strong> Maintain identical business positioning across all platforms<\/p>\n<h2>Measuring AI Optimization Performance and ROI<\/h2>\n<p>Traditional SEO metrics don&#8217;t capture the full impact of AI optimization. I&#8217;ve developed specific KPIs that accurately measure AI agent performance and business impact.<\/p>\n<h3>AI-Specific Performance Indicators<\/h3>\n<p><strong>Citation Rate Tracking:<\/strong><\/p>\n<ul>\n<li>Monitor mentions in AI-generated answers across platforms<\/li>\n<li>Track branded vs. unbranded citations<\/li>\n<li>Measure citation context (positive, neutral, competitive)<\/li>\n<\/ul>\n<p><strong>Featured Snippet Capture:<\/strong><\/p>\n<ul>\n<li>Track featured snippet wins across target keywords<\/li>\n<li>Monitor snippet format performance (lists, tables, paragraphs)<\/li>\n<li>Analyze snippet retention rates over time<\/li>\n<\/ul>\n<p><strong>Entity Recognition Metrics:<\/strong><\/p>\n<ul>\n<li>Search your brand name in AI platforms monthly<\/li>\n<li>Track entity relationship accuracy in AI responses<\/li>\n<li>Monitor competitor mention frequency alongside your brand<\/li>\n<\/ul>\n<h3>Analytics Setup and Conversion Tracking<\/h3>\n<p><strong>Traffic Source Identification:<\/strong> Configure analytics to identify AI-driven traffic sources:<\/p>\n<ul>\n<li>Set up UTM tracking for AI platform referrals<\/li>\n<li>Create custom segments for AI-driven user behavior<\/li>\n<li>Monitor engagement metrics for AI-sourced traffic<\/li>\n<\/ul>\n<p><strong>Conversion Attribution:<\/strong> Track business impact from AI optimization efforts:<\/p>\n<ul>\n<li>Lead quality scores for AI-sourced traffic<\/li>\n<li>Revenue attribution to AI citations<\/li>\n<li>Brand awareness lift from AI mentions<\/li>\n<li>Customer acquisition cost reduction through AI visibility<\/li>\n<\/ul>\n<p>The measurement framework should focus on business outcomes rather than vanity metrics. B2B SaaS companies typically see a 23% improvement in lead quality when prospects discover them through AI-generated answers compared to traditional search results.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI agents are fundamentally changing how content is discovered, processed, and served to users. Unlike traditional search engines that rely primarily on keyword matching and&#8230;<\/p>\n","protected":false},"author":1,"featured_media":858,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-859","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-seo"],"_links":{"self":[{"href":"https:\/\/www.stridec.com\/blog\/wp-json\/wp\/v2\/posts\/859","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.stridec.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.stridec.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.stridec.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.stridec.com\/blog\/wp-json\/wp\/v2\/comments?post=859"}],"version-history":[{"count":0,"href":"https:\/\/www.stridec.com\/blog\/wp-json\/wp\/v2\/posts\/859\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.stridec.com\/blog\/wp-json\/wp\/v2\/media\/858"}],"wp:attachment":[{"href":"https:\/\/www.stridec.com\/blog\/wp-json\/wp\/v2\/media?parent=859"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.stridec.com\/blog\/wp-json\/wp\/v2\/categories?post=859"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.stridec.com\/blog\/wp-json\/wp\/v2\/tags?post=859"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}