Professional Profile

I've been building content systems since 1977 - starting with assembler code, long before "CMS" was a term. Co-authored Superbase, a database and content management system that predated the CMS category. Worked on the BBC's electronic newsroom system. Over a decade with Adobe AEM, recent years with Edge Delivery Services.

Working with EDS taught me something unexpected: the structure that makes content work for AI agents is mostly what everyone needs. The patterns that break for AI agents—hidden state, ephemeral notifications, incomplete information—also break for humans with disabilities, cognitive load, or non-ideal conditions. That insight became Machine Experience (MX).

I help organisations make better strategic decisions about Adobe Experience Manager and Edge Delivery Services in this new reality. After working on content systems for BBC, Twitter, Nissan-Renault (hundreds of websites), Ford, MediaMonks, and others, I've learned that successful implementations come from asking the right questions before building anything—particularly now, as AI agents fundamentally change how web experiences are consumed.

Recent Adobe Experience Manager implementations demonstrate this approach: the Generate Variations feature reduced banner creation from weeks to days whilst maintaining human strategic oversight, delivering many variations with much higher click-through rates. Success came from agent-ready foundations - semantic structure, explicit state, machine-readable metadata - that let AI handle pattern generation whilst humans controlled messaging and brand alignment.

My work centres on what I call "clarity infrastructure"—systems that make state explicit, feedback persistent, and information complete. Using Cloudflare's global edge network and Adobe EDS, I've implemented this principle at scale: enriching HTML with explicit state attributes, enforcing semantic structure, providing machine-readable Schema.org data, and ensuring critical information exists in served HTML before JavaScript execution. This creates agent-ready foundations that work for CLI agents, API agents, browser agents, and every human user through universal design patterns.

The business urgency is real: Amazon, Microsoft, and Google all launched agent commerce in early 2026. First movers in each sector who build genuinely agent-ready systems will capture agent-mediated transactions while competitors struggle with silent failures. But here's the efficiency multiplier: agent compatibility and accessibility improvements are identical work. Every pattern that helps agents—semantic HTML, explicit state, persistent errors—also helps screen reader users, keyboard users, and anyone in non-ideal conditions.

I work with teams facing complex AEM and Edge Delivery Services decisions—whether evaluating EDS adoption, planning AI agent integration, or reviewing architectural approaches for agent readiness. My focus is strategic guidance that prevents expensive mistakes and builds internal capabilities. The BBC, Twitter, and Nissan-Renault implementations weren't successful because of technical complexity. They worked because we developed frameworks that helped distributed teams make consistent decisions independently. That principle shapes everything I do.

My approach combines practical implementation experience with deep understanding of AI system internals. I write extensively about the statistical foundations of AI agents - how next-token prediction produces both capabilities and hallucinations, why linguistic tokenisation creates functional inequities, and how weighted averaging determines which HTML patterns agents can reliably process. This technical depth informs architectural decisions: knowing that agents perform statistical pattern-matching rather than "understanding" content explains why explicit state attributes and semantic structure matter more than visual design.¹

I take on interim consultancy roles and advisory engagements where strategic experience makes the difference:

• Plan Reviews - identifying gaps between intention and reality before implementation begins, particularly around agent compatibility
• Architecture Strategy - developing frameworks that balance corporate control with team flexibility while ensuring agent-ready foundations
• AI Integration - ensuring automation enhances rather than complicates workflows, with focus on clarity infrastructure
• Team Mentoring - building strategic thinking capabilities that outlast any single project
• Audit - where things went well, and where they could be improved

I have established first AEM practices from scratch. Strategic decisions prevented platform crashes and delivered significant cost savings. Teams gain capabilities to maintain and evolve solutions independently.

Industry Perspective

As a member of Boye & Company's CMS Experts Group and regular industry speaker, I stay connected with emerging trends while grounding recommendations in proven approaches. My work demonstrates a practical reference model for what the Agent Ecosystem is standardizing: interoperable, multi-vendor systems where clarity serves everyone. Known in CMS circles as "The AEM Guy"—a credential earned over a decade architecting Adobe platforms—though I prefer being known for helping teams make sound strategic decisions that prepare for agentic workflows using MACH principles of modularity, openness, and composability.

Since 1977, I've been solving content distribution problems across every generation of technology - from assembler code through Superbase, BBC systems, Adobe AEM, and Edge Delivery Services. All variations of the same fundamental challenge: content that works for different consumers. Now those consumers include AI agents, and the patterns I've been refining for nearly five decades apply more than ever.

I work exclusively through Digital Domain Technologies, focusing on engagements where experience and objectivity matter most. Available for interim consultancy roles, advisory projects, and strategic reviews—not seeking full-time positions.

If you're evaluating Edge Delivery Services for agent readiness, planning major AEM changes in an AI-native world, or need architectural guidance that prevents problems before they're expensive to solve, let's talk about how strategic partnership might help.

Strategic advantage comes from having the right frameworks in place before you need them—frameworks that recognize "agent-ready" means accessible, observable, and universally comprehensible. That's where experienced advisory makes the difference.