Blog
Thoughts on machine experience, AI agents, and the semantic web.
Articles from Tom Cranstoun and the CogNovaMX team on Machine Experience, AI agent behaviour, metadata patterns, content architecture, and the evolving relationship between websites and the machines that read them.
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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.
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WPP Built Open. Omnicom Built Omni. Both Left the Same Layer Open
The holding companies are spending fortunes on proprietary AI platforms. None of them writes a provenance record into the file, the layer that survives when content leaves the platform. That layer is open, and that is the point.
Microsoft Made the Trace Valuable. It Left It Unsigned.
Frontier Tuning turns the record of an agent's work into training data an organisation owns. That same record is the evidence of what the agent decided. Microsoft makes the trace valuable; it says nothing about how anyone establishes what is in it, who made it, or whether it has been altered.
Software Agreed the Deal. Who Is Accountable?
Software is starting to negotiate contracts with other software, no human in the exchange. The answer is not a human in the loop but a human in command: a signed mandate the agent executes under, and the records that prove it.
Read Is Not the Same as Trusted
Common Crawl's AI Visibility Audit confirms that reachability decides whether a site enters LLM training. It does not address whether the ingested content can prove where it came from. That is the MX layer.
The Crawl Still Speaks English
Most AI models learn the web through one archive, and that archive is overwhelmingly English. Two years on, the share has barely moved. You cannot fix the model from outside, but you can stop it guessing about your non-English content.
Strip the Marks, Lose the Word
English speakers drop accents out of habit, and in English it rarely matters. In Vietnamese it deletes the word. When marks are stripped before a machine reads the text, no translation can recover them, and a provenance system that signs the wrong bytes breaks on exactly the languages that need it most.
Who answers when the machine decides?
Browsers and assistants increasingly classify, gate and summarise the web by inference — decisions made about a site, in private, with no accountable author. Set against where AI regulation is heading, the case for declared, attested signals over hidden judgement.
Salesforce Buys Contentful, and the Question Is Who Owns Agent-Ready Content
The acquisition confirms machine-readable content matters. The open question is whether agent-readiness becomes a platform feature you rent, or an open standard - content that manages itself - that any system can read.
Declare Once, Work Everywhere
Every machine reading your content is guessing what it means and how to treat it. One attested record, written once and readable by any system that encounters the file, ends that. This is what we do and why it matters now.
The Inspector You Can Audit Yourself
The public PDF inspector at mx.allabout.network checks one file at a time for free. The same detection core is now available as a command-line tool for accredited operators, and as a hard gate in our own publishing pipeline. Four surfaces, one source of truth, all readable in the open.
An MX Audit, Read by an Engineer
A walk through an MX audit deliverable from an engineer's perspective: one scoring vocabulary across the scorecard, the contents page on its own page, findings in a sibling sidecar, regression tests, and the contract that makes the PDF MX Compatible.
An MX Audit, On Your Desk
How an MX audit report is shaped around the conversation it is meant to have with you. One scoring language. Cover, contents, report, each on its own page. Reviewer notes in a sibling file, not inside the report.
How an MX Audit Proves Itself
What an auditor reading an MX deliverable can verify without trusting our word for it: the evidence chain inside the PDF, the sidecar pair on disk, the regression test that proves the contract on every render, and the interactive inspector that walks the chain end-to-end in a browser.
A PDF That Can Prove Itself
Every PDF this site produces now carries its own evidence chain inside its XMP packet. Open the file with no network and no source repo; an inspector can still read who made it, when, what it was rendered from, and what the chain is honestly silent about.
The Padlock Attests the Pipe, Not the Page
The TLS padlock has been the web's most-misread icon for twenty years. It says the connection is encrypted; it has never said the page is true. Closing the gap takes content that carries its own evidence and a verification step that runs on the reader's side.
Oversight Needs Evidence: Pope Leo's AI Encyclical and the Machine-Readable Web
Pope Leo XIV's AI encyclical asks for oversight, informed users, and accountability. Each is a documentation problem before a moral one, and that is where MX fits.
Provenance You Can See: Why Content Should Carry Its Own Evidence
Trust on the web has always been a guess. This is how content can carry checkable evidence of where it came from, and why both readers and AI agents need it now.
Nobody Asked Gutenberg for His Sources
Nobody asked Gutenberg who wrote it, who checked it, or who was liable. AI brought the questions back. A playful take on EU rules versus free-speech absolutism.
The Internet in 2031: What a Human Observer Sees
A speculative field note from five years on: how the web looks to an ordinary person once agents, provenance, and verifiable content became ordinary.
Files away from their source: what Universal Cart reveals about MX
Google's Universal Cart is for the web. MX is for files. The shape of the problem the cart makes visible in retail repeats everywhere else, including off the web and on paper.
When the AI world realised it needed standards
Two years ago, Tom Cranstoun wrote that AI's real impact on the web is as a content consumer. Vancouver in March 2026 confirmed it. The Gathering is the answer.
MX and Cryptocurrency: Drawing the Line
Cryptocurrency is the part of the blockchain world MX has the least to do with. The integrator post for the use-cases set on MX, blockchain, NFTs and crypto.
What Blockchain and Crypto Have to Do with MX
MX is not a blockchain or a crypto project. It uses the same primitive (public-key cryptography) for a different job, with no ledger, no consensus, and no token.
Is MX Useful to Blockchain?
When a chain is used as a record system rather than a currency, MX is the discovery and structure layer that makes the on-chain record's content readable by machines.
NFTs and MX
An NFT proves ownership of a token. It does little about whether the content the token points at still exists, is unaltered, or can be read. That gap is MX's job.
Why Machines Need Human Creativity
Machines extend and execute; they do not originate. The arrangement that produces work worth signing keeps the person at the start and the end, with the machine doing what it is good at in the middle.
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 behaviour is deterministic instead of guessed.
Build Content Systems That Machines Can Trust
SEO and GEO optimise 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.
What Google's web.dev agent guidance does not touch
Google's 1 May 2026 web.dev guide tells developers to make their pages agent-friendly. The advice is sound. It also stops at the rendered HTML page. Provenance, authentication, rights, lifecycle, and off-web carriers are not in scope. MX is.
Why your AI agent gives you a different answer every time
If you treat AI as magic, you get magic's reliability. The fix is to stop writing instructions and start writing contracts.
Not All Agent-Readiness Scores Measure the Same Thing
Two prominent tools gave the same site a score of 33 and 100 in the same week. Neither was wrong. Here is what is actually being measured, and what to do with that information.
Tom Cranstoun Launches MX: The Handbook
Tom Cranstoun's MX: The Handbook turns a 2024 CMS Critic insight into a full implementation framework for the AI agent era. A practical guide to building websites that AI agents can actually use.
DITA and MX: A Comparison
A structured comparison of the Darwin Information Typing Architecture and Machine Experience, identifying where they overlap, where they differ, and what MX draws from DITA.
A Standard That Knows What It Isn't
A preview of Chapter 21 of MX: The Protocols, why the MX standard stays small, defers to DCAT, Schema.org, EXIF, and IETF, and why that restraint is the architecture, not a limitation.
The Agent Web Looks a Lot Like 1995
Four agent protocols, four vendors, and the standards-community gap that matters more than any of them. Why The Gathering exists, and how to show up.
MX: The Handbook Is Here
The practical implementation guide to Machine Experience, making your website work for AI agents, screen readers, and everything in between. Available now as PDF and print.
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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