I’ve been using AI for over two years, progressing from clever auto-completion through vibe coding to agentic engineering. Recently I iterated for a couple of weeks over a prototype application. The biggest problems came down to a single word, a term I’m found myself using more than any other over the last few weeks. And it’s not exclusive to a single project. It’s a term that’s been used across coding work, research work, everything.
When I was at school in the late 1980s, our school got a computer. Just one, in the library. I remember using it for some document I wrote, I can’t remember what, on a floppy (yes, actually “floppy”) disk. At university we had computer labs, but I still wrote my undergraduate dissertation on an electronic typewriter. My MA was written on the computer and during my PhD I published two articles on an “electronic journal”. But computers had not had a significant impact on education.
Fast forward to now, and most schools have Google accounts for their students, even at primary school. During lockdown lessons were delivered on Microsoft Teams. Online applications are not only used for teaching Computer Studies, learning Python, HTML and CSS. They’re also used for maths homework and times tables, as well as pointing to videos on BBC website and others.
But computers and mobile devices have just replaced text books and written homework. They have changed where learning happens, not how learning happens. But I think AI should create a monumental change in the steps through education. The question is how long it takes academics to realise it.
Last week I spoke at Engage conference in KAA Arena Ghent. I’ve been attending Engage since 2010 and only missed one of the annual conferences since then. It was my first conference, so it’s always held a dear place in my heart. Although it’s many years since I developed for Domino REST API, my work over recent months gave me experience to build innovative proof of concepts using Domino REST API and experience of various models and AI agents to integrate the API with various interfaces.
AI usage has evolved over the years as the power and limitations of LLMs and agents have progressed. The community has learned that they needed ways to provide additional abilities. Coding clients have adapted to provide those capabilities. Thankfully the IT ecosystem has changed dramatically since the birth of enterprise IT, so open source and standards have become the default mechanism instead of a last resort after community pushback. But we’re starting to see yet another approach, one I’ll call Token Engineering.
Later this month I will be speaking at Engage 2026, delivering the session Super-Charging Your AI with Domino REST API on Wednesday 24th April at 14:05 in Room E. The session will showcase a variety of AI techniques for integrating Domino applications with agentic interfaces or agentic engineering. The session demonstrates the art of the possible, but is primarily designed to provide the information needed to make the right choices when integrating Domino data with agentic solutions.
AGENTS.md has become the de facto standard for directing your agent. Claude Code had CLAUDE.md but with AGENTS.md becoming part of the Agentic AI Foundation last year, even Claude Code now recognises and uses AGENTS.md. But the benefits of AGENTS.md are now being discussed and challenged in academic circles. However, maybe what’s being discussed is just the first iteration. And just as the AI world went through Prompt Engineering and then Context Engineering, maybe the next phase (or a next phase) is “AGENTS.md Engineering”.
More than ten years ago, Christian Guedemann and I worked with various IBMers to try to get a free Domino server entitlement for developers. It took time - not an unacceptable amount of time, in my personal opinion - but we got more than we hoped. The dev and test server license is no longer available, because licensing for customers changed. Some are critical of that decision. But none, as far as I’m aware, have demonstrated taken the high-agency mindset that Christian and I demonstrated and which is crucial to effect change.
The ability of AI to generate software applications has taken gigantic leaps forward in just the last few months. A team of agents using Claude Opus 4.6 wrote a C compiler with minimal human interaction. In the press release for Opus 4.6 Anthropic build Claude with Claude. I personally use coding agents more and more, and I’ve seen significant rapid application development with AI.
It was only late last year that I heard the term "high-agency mindset", even though it's nearly a decade old. But after learning about it, I started to recognise signs to some extent in myself and some others I know. It became understandable why high-agency individuals surround themselves with other high-agency individuals, and why they can become frustrated with low-agency individuals. Where others see blockers and wait for (or expect) someone else to clear them, high-agency mindset individuals can see solutions or expect people to take responsibility for resolving them - or else they just go around them. It’s a mindset that aligns well with the concept of Directly Responsible Individuals or DRIs.
But it also became apparent that AI could have a big impact on the future of the two high-agency and lower-agency individuals.
About 20 years ago there was a major shift in document processing which had profound repercussions for the AI world, years before it started. With Office 2007, Microsoft made a major change in their document formats. Up until that point, Microsoft had preferred closed, proprietary, customer lock-in of content into silos of information. But during the 2000s the rise of Apache OpenOffice and its open document formats, along with governmental demands for interoperability, put pressure on Microsoft to introduce OOXML. Of course it had added benefits - competition alone rarely encourages change. The new format was up to 75% smaller, improved corruption recovery and improved security.