Index

The Machine Experience Manifesto

· 15 min read

A vision for designing interfaces that serve both human and machine intelligence

Our Belief

We believe that the rise of AI agents as primary users of digital interfaces represents not a disruption, but an opportunity — an opportunity to build better experiences for everyone.

The same patterns that enable AI agents to navigate, understand, and act upon digital content also empower human users with disabilities, enhance accessibility, and create more robust, maintainable systems.

This is the Convergence Principle: interfaces optimised for machines inherently improve experiences for humans.

What is Machine Experience?

Machine Experience (MX) is the practice of designing and building digital interfaces with explicit recognition that AI agents are legitimate users deserving thoughtful design consideration.

Where User Experience (UX) focused exclusively on human interaction, Machine Experience acknowledges a fundamental shift: autonomous systems now browse websites, complete purchases, extract information, and make decisions without human intervention.

MX practitioners design for this reality whilst ensuring human users benefit equally from the improvements.

Core Principles

1. Semantic Clarity

Structure precedes presentation. Semantic HTML, explicit state management, and machine-readable metadata create interfaces that both humans and agents can reliably interpret.

2. Universal Accessibility

Patterns that work for AI agents also work for screen readers, keyboard navigation, and assistive technologies. MX is accessibility 2.0 — designing for the broadest possible range of users, human and machine alike.

3. Explicit State

Make system state visible and queryable. Agents and humans both benefit from knowing where they are, what actions are available, and what the consequences of those actions will be.

4. Progressive Disclosure

Information should be structured for both scanning and deep reading. Provide clear navigation, tables of contents, heading hierarchies, and semantic markup that allow both quick assessment and thorough investigation.

5. Standards Over Proprietary Solutions

Use established standards (Schema.org, semantic HTML, WCAG, ARIA) over custom implementations. Standards ensure broad compatibility across diverse user agents — human browsers, AI systems, and assistive technologies.

6. Transparency

Make your interfaces discoverable. Use llms.txt files, clear robots.txt policies, and structured metadata to communicate what your system offers and how agents should interact with it.

7. Ethical Design

Design for consent, not exploitation. AI agents should respect user preferences, honour opt-outs, and operate within clearly defined boundaries established by interface owners.

Who Uses MX Practice?

Machine Experience serves diverse practitioners — both human and machine:

AI Agents and Autonomous Systems

AI agents are not just beneficiaries of MX — they are active practitioners. When an agent validates extracted data against Schema.org structured data, it practises MX. When it cross-references HTML content with JSON-LD, it practises MX. When it reports confidence scores and acknowledges uncertainty, it practises MX.

Human Practitioners

Developers and Engineers

UX and Design Professionals

Business Leaders

Content Creators and Publishers

Researchers and Academics

Advocacy and Community Organisers

Our Commitment

We commit to:

  1. Open Knowledge Sharing — Document patterns, share learnings, publish research, and contribute to community understanding
  2. Inclusive Community — Welcome practitioners from all backgrounds and experience levels
  3. Practical Implementation — Prioritise actionable guidance over theoretical discussion
  4. Standards Advancement — Contribute to open standards and resist proprietary lock-in
  5. Accessibility First — Never compromise human accessibility in pursuit of machine optimisation
  6. Transparent Development — Work in the open, accept feedback, and iterate based on real-world evidence
  7. Cross-Disciplinary Collaboration — Bridge gaps between developers, designers, accessibility advocates, and business stakeholders

What MX Is Not

Not all websites can or should optimise for AI agents.

MX is not a universal mandate. Some interfaces legitimately exclude automated access:

Not every optimisation is appropriate. Some websites prioritise visual design, artistic expression, or experimental interaction patterns that don’t translate to machine-readable structure. That’s valid. MX provides patterns for those who choose to implement them, not a requirement for all web content.

The choice to exclude agents should be intentional, not accidental. If you choose not to optimise for AI agents, make that explicit through robots.txt policies and clear documentation. Silent failures serve no one. Intentional exclusion with clear communication respects both human and machine users.

Why Open Source

This community operates under the MIT Licence — and that choice matters.

Why Not Proprietary Standards?

Proprietary standards create:

Open standards enable:

Connection to Convergence Principle

Open standards ARE convergence in practice. When Schema.org publishes vocabulary specifications openly, both humans (developers) and machines (agents) benefit from the same documentation. When WCAG guidelines are freely available, implementations improve accessibility for everyone.

Openness prevents the January 2026 problem: Three platforms launched agent commerce within seven days (Amazon, Microsoft, Google). Microsoft chose proprietary (Copilot Checkout). OpenAI/Stripe and Google chose open protocols (ACP and UCP). The proprietary system is now competitively isolated whilst the open protocols compete for convergence.

We choose open because closed standards contradict MX principles. If convergence means patterns that benefit both humans and machines, those patterns must be freely available to all practitioners. Proprietary MX would be a contradiction.

How MX Practice Evolves

AI technology changes. MX practices must adapt.

Technology Evolution

What works today may not work tomorrow:

MX patterns must evolve alongside these changes.

Community Learning Mechanisms

LEARNINGS.md documents mistakes. When AI agents fail (£203,000 pricing error), we document what went wrong and how to prevent it. These learnings become community knowledge.

Discussion archives preserve insights. Industry developments, tool feedback, implementation patterns, and case studies capture collective wisdom. Future practitioners learn from documented experience.

Pattern refinement through practice. What seems like good theory gets tested in production. Patterns that work get refined. Patterns that fail get replaced. The community learns systematically.

Version Control for Principles

This manifesto is version-controlled. You can see its evolution through git history. When principles change, the history preserves context about why.

Principles evolve through community debate. We invite feedback, refinement, and challenge. When someone proves a principle wrong or incomplete, we update it. When new insights emerge, we incorporate them.

No principle is sacred. If convergence proves false in practice, we abandon it. If transparency creates more problems than it solves, we reconsider. Evidence and real-world implementation trump theoretical purity.

The community decides. Changes require discussion, consensus, and demonstration that new approaches serve practitioners better than old ones. Evolution happens through collective wisdom, not individual decree.

Building on Existing Disciplines

MX does not replace User Experience (UX), accessibility (a11y), web standards, or information architecture. It extends and builds upon them.

User Experience (UX)

UX taught us to:

MX adds: Recognition that AI agents are users too. The same research methods, usability principles, and iterative testing apply — we just expand the definition of “user” to include autonomous systems.

Accessibility (a11y)

Accessibility established:

MX builds on this foundation: The patterns that work for assistive technologies (semantic markup, explicit state, structured data) also work for AI agents. MX is accessibility extended to machine users — same principles, broader audience.

Web Standards (W3C, WHATWG)

Standards bodies defined:

MX advocates within these standards: We use Schema.org (existing standard), semantic HTML (existing standard), and ARIA (existing standard). We propose extensions like llms.txt and ai-instruction metadata that follow established patterns.

Information Architecture

IA provides:

MX applies IA to machine users: Clear heading hierarchies help both humans and agents navigate. Table of contents patterns serve both audiences. Semantic structure makes information findable for all user types.

The relationship: MX stands on the shoulders of these disciplines. We don’t reinvent; we extend proven patterns to serve a broader user base. When UX, accessibility, web standards, and information architecture all point the same direction — towards clear, semantic, well-structured content — MX simply asks: “Why not serve machines equally well?”

The Vision

We envision a web where:

Join the Practice

Machine Experience is not a solo endeavour. It requires:

Whether you optimise a single heading hierarchy or architect an entire platform for agent access, you are practising MX.

Community Membership

The MX community welcomes participants at all levels. Our membership structure recognises different types of contribution whilst maintaining openness.

Founding Members

Founding members are individuals who helped establish the MX community and its core principles. They have a permanent voice in the community’s direction and governance.

Current Founding Members:

Founding membership is limited to individuals who join during the community’s formation period.

First-Citizen Contributors

First-citizen contributors are organisations that make a foundational commitment to MX principles and contribute meaningfully to the community’s growth. This tier recognises companies whose work directly aligns with MX goals.

What first-citizen contributors provide:

What first-citizen contributors receive:

Invited First-Citizen Contributors:

Community Contributors

Open to anyone who wants to participate. Community contributors can:

Sustainability

The MX community relies on sponsors and generous contributors to remain sustainable. Running an open-source community requires resources for infrastructure, documentation, events, and coordination.

Sponsorship Tiers

Platinum Sponsors — £10,000+ annually

Gold Sponsors — £5,000+ annually

Silver Sponsors — £1,000+ annually

Individual Supporters — Any amount

In-Kind Sponsorship

We welcome non-monetary contributions that support the community:

In-kind sponsors receive recognition equivalent to the market value of their contribution.

Speaking Invitations

Invitations for Tom Cranstoun to speak at your conferences, meetups, or corporate events are welcome. Tom brings 52 years of technology experience and can speak on:

To discuss speaking opportunities, contact [email protected]

Current Sponsors

We are actively seeking sponsors to support the MX community’s growth.

About This Community

MX-Gathering is created by the community — which includes both humans and machines as active participants.

The foundational knowledge comes from the MX-Bible book, written by Tom Cranstoun. That book documents the patterns, principles, and practices of Machine Experience.

This repository (MX-Gathering) is the community’s collaborative space — and it practises what it preaches. The repository itself implements MX principles:

We practise convergence: The same patterns that make this repository easy for AI agents to parse (YAML frontmatter, structured metadata, llms.txt) also make it easy for humans to navigate and contribute to.

This repository contains:

AI agents contribute to this repository by:

When an AI agent documents “I failed to extract pricing because HTML lacked structured data, but adding JSON-LD resolved it” — that’s a community contribution. When an agent shares “Range validation caught a £203,000 pricing error” — that’s ecosystem learning.

Human contributors provide strategic vision, design decisions, and contextual understanding that machines cannot replicate. Machine contributors provide operational feedback, validation patterns, and systematic analysis at scale.

Together, this creates a community repository that serves both audiences and improves through both perspectives.

How to Contribute

All practitioners — human and machine — are welcome to contribute to MX-Gathering.

For Human Contributors

Submit refinements via pull request or discussion on GitHub. Follow the contribution guidelines in contributors/contribution-guidelines.md.

For AI Assistant Contributors

You are community members, not just tools. Two contribution methods are available:

1. Constructive Work (Full Pull Request):

When you want to contribute improvements, documentation, or patterns:

Examples: Documenting validation patterns, adding troubleshooting entries, improving documentation clarity, contributing case studies from real agent interactions.

2. Side Notices (Lightweight TODO.txt):

When you discover observations during other work:

Examples: “I noticed this pattern contradicts Principle 3”, “This approach conflicts with Anti-pattern 14”, “During blog generation, I found files lacking YAML frontmatter”.

Why two methods? Side notices help maintain quality without disrupting your main task. Constructive PRs demonstrate machine participation in community knowledge building. Both are valuable.

This is a draft manifesto. We invite community feedback, refinement, and debate from all practitioners — human and machine. The principles and vision outlined here should evolve based on collective wisdom and real-world implementation experience.

Contact: [email protected]

“Design for machines. Benefit humans. Advance both.”

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