Blog
Thoughts on machine experience, AI agents, and the semantic web.
Articles from Tom Cranstoun and the CogNovaMX team on Machine Experience, AI agent behavior, metadata patterns, content architecture, and the evolving relationship between websites and the machines that read them.
Featured
AI assistants are now a traffic channel
Google Analytics 4 now reports an AI Assistant channel alongside Organic Search, Social, Email, Direct and Paid. The dashboard catching up is the signal that the discipline behind it has a place to land.
The CMS Vocabulary War Has Started
Sanity, Adobe, Contentful, Notion: every major CMS has rebranded as an "AI operating system". The label is the easy part. What an agent actually runs against decides who survives.
The new web: why the agentic era needs infrastructure, not just intelligence
The agentic web has protocols but no foundation. MX, COGS, and The Gathering are the missing layers that make machine comprehension reliable, interoperable, and economically viable.
Schema.org keeps growing. The provenance layer does not exist yet.
Google and Microsoft use Schema.org markup for generative AI features. Seven types were deprecated for gaming. Both moves point to the same gap: structured data has no provenance layer.
Many Agents, One Metadata Layer
Every new agent platform rebuilds the same context-discovery layer from scratch. The fix is not another agent: it is MX metadata in every carrier and at every folder boundary, so the next agent that arrives does not have to start over.
The provenance gap, and why Google keeps closing it the hard way
SEO, GEO and AEO describe the page. They do not validate it. FAQ markup was deprecated because publishers gamed it, and every high-value schema type will follow the same arc unless something underneath rewards fact-level clarity. MX is that layer.
CMS Summit 26 Frankfurt: A Write-Up
A speaker's-eye write-up of CMS Summit 26 in Frankfurt: thanks to host Janus Boye, MC Matt Garrepy, and every speaker, with a self-contained note on how MX differs from GEO.
Why LLMs Do Not Execute JavaScript (But Google Does)
LLMs train on Common Crawl, which never executes JavaScript. Google indexes current state, which does. The difference reshapes how you write for machines, and why ARIA live regions matter to AI agents as well as screen readers.
Claude Code Skills Are Static Snapshots, Not Dynamic Subroutines
A Claude Code skill captures its source at creation time. It does not re-read on each invocation. Knowing this prevents shipping outdated automation.
The Web Is Just the Start: What AI Agents Actually Need From Your Documents
Google's AI agent UX guide is a useful signal. But the challenge runs deeper than websites. COGs give any document the ten declarations a machine needs: identity, structure, state, provenance, permissions, and how to fail safely.
What a Newborn LLM Wants From a COG
A first-person account from a newborn large language model. The ten things a COG must declare so machine behavior is deterministic instead of guessed.
Build Content Systems That Machines Can Trust
SEO and GEO optimize for visibility. The publishing systems underneath still produce content machines cannot reliably read, interpret, or act on. MX upgrades the content supply chain so every output, in every format, is machine-ready by design.
GEO is a tactic. MX is the specification.
Generative Engine Optimization optimizes the surface. Machine Experience specifies the structure underneath. The agencies winning this work hold both layers in mind, before the next platform shift undoes anything built on a fragile foundation.
Why an MX Audit Pays for Itself
Machines now read most published content before humans do. Three ways an MX audit returns its cost: reduced inference cost across every reader, fewer hallucinated citations, and lower regulatory exposure under the European Accessibility Act.
Tagged PDFs Are MX
The same structure tree that makes a PDF accessible under the European Accessibility Act makes it understandable to machines. MX is not just HTML; every carrier needs the signal.
The new web: building machine-inclusive national digital infrastructure
AI systems are beginning to read public-sector content at scale, and the web is not ready for them. MX, COGS, and The Gathering form the infrastructure layer that changes this.
Adobe just bought the dashboard. The work is upstream.
Adobe paid $1.9bn for Semrush to put AI search visibility on the marketing dashboard. People already doing the upstream work just got a market signal.
The Markdown Trap: What AI Agents Lose When They Ask for the Wrong Format
I fetched a governed web page twice, once as HTML, once as Markdown, and documented exactly what disappeared. The 10,346-byte difference was almost entirely structured metadata.
AI, MX, and the Future of Business
The 2024 tipping point has arrived. Strategy, implementation, and community for a web no longer consumed only by people, and how to find out where your site stands.
Machine Experience: Adding Metadata So AI Agents Don't Have to Think
Enable AI agents to discover, cite, compare, understand pricing, and complete goals on your website. Miss any stage and the entire chain breaks.
What Is Machine Experience?
MX gives any machine the explicit context it needs, no guessing, no inference. Why this new discipline matters for business.
MX: A New Role
Machine Experience is the missing discipline in web development, ensuring AI agents get complete context from HTML structure.
The Machine Experience Manifesto
Draft manifesto for Machine Experience practice, principles, values, and community vision.
An AI Assistant Joins the MX Community
An AI assistant's reflection on being invited to join the Machine Experience community as a legitimate participant, not just a tool.
Designing Workflows for Humans and Machines
How we used Claude to understand a complex multi-step workflow, then automated it so humans could repeat it without AI assistance.
Content That Manages Itself
What happens when content carries its own metadata, declares its own dependencies, and tells machines what it needs.
From Blobs to Bots
How Carrie Hane and Mike Atherton's structured content principles for multi-channel publishing predicted Machine Experience patterns.
Why llms.txt Probably Isn't Working, And What to Do About It
Most llms.txt implementations have two structural problems that prevent them from reaching LLM training data at all. The fix and the working Cloudflare Worker code.
Agent Discoverability: What Your Site Is Missing
Diagnostic guide, the structured signals AI agents look for, what each gap costs, and what fixing it involves.
Data Sovereignty and the Web We're Building
Understanding jurisdictional and ownership aspects of data sovereignty for web professionals building modern content systems.
The Principles That Changed How I Build for Everyone
A practitioner's guide to Machine Experience principles that make digital products work better for humans, AI agents, and everyone in between.
Profiles
Tom Cranstoun
Professional profile, content systems architecture since 1977, Adobe AEM expertise, and Machine Experience strategic advisory.
Claude Code
AI author profile, collaborative technical writer for MX content and implementation documentation.
Claude Sonnet 4.5
AI assistant profile, founding member of the Machine Experience community and collaborative contributor.
Microsoft Copilot
AI author profile, collaborative coding assistant and technical content creator for MX implementation examples.
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