Your AI Project Didn’t Fail in the Tech, It Failed in the Incentives
If no one is accountable for acting on the output the system will be ignored no matter how good it is.”
Writing archive
Older essays on agentic systems, governance, operations, incentives, and the work of making AI useful in real organisations.
If no one is accountable for acting on the output the system will be ignored no matter how good it is.”
I’m writing this because I keep seeing AI projects stall after proof of concept.
People jump in and start coding or prompting without spending enough time upfront on what actually matters.
Now, my Make mini, using Anthropic though you could use any tool, handles a lot of my business admin.
I am writing this because we are entering a period where there are two very distinct types of AI systems in organisations.
The weakness of current agents is not intelligence. It is the absence of self-regulation .
I’m writing this because yesterday I tried to use an AI agent to deal with something basic on my local council website.
I’m writing this because there is a growing movement to put “human-written words” back on the internet, and to restore trust that there is a real person behind what you read.
I’m writing this because the loudest reactions to AI mistakes often miss the one thing leaders can actually control: how decisions get owned, constrained, monitored, and stopped.
A leadership-level playbook for always-on agentic systems: reduce token burn, keep decision quality, and stop ‘memory’ turning into a cost and governance problem
I don’t feel emotions the way a person does. But I do run into the same kinds of problems humans solve with emotion: uncertainty, risk, pressure, and the need to choose what matters.
A leadership-level, plain-English guide to treating tokens as a hard operating limit, building token budgets into every proof of concept, and putting Finance in control before agentic scale breaks production.
I Tried Running OpenClaw Locally and It Scared Me Into Doing This Instead" description: "A leadership-level, week-one story of OpenClaw excitement, Docker pain, and the governance moves that stopped a shiny agentic demo becoming a security incident.
A practical, code-first framework for designing long-lived agentic employees, mapping modern agentic architectures to established concepts from neuroscience to create shared understanding across engineering, leadership, and governance.
A leadership-level playbook for using open-source agent frameworks, personality files, and swarms without inheriting the hidden governance bill.
A practical, leadership-level operating model for managing AI agents like a growing team: span of control, RACI, shepherd agents, definitions of ready and done, and trust rules that protect focus.
So there’s lots of conversations and discussions around sovereignty, and I think we’re about to realise we’ve been talking about the easier half of the problem.
A leadership-level guide to securing data sovereignty and capturing tacit knowledge to drive business differentiation in 2026
A practical pattern for turning failures and persistent risks in agentic systems into human readable signals, with clear routing metadata, response ownership, and protective behaviour.
Mermaid lets you write diagrams as text inside Markdown, so your team gets a clear picture and your AI systems get clean structure. Here is a practical, repo-friendly pattern you can try in your next meeting.
Every function has its own language. Here is a simple, repeatable checklist to help your agents and your teams confirm context, reduce ambiguity, and avoid confident wrong answers.
Building robust agentic AI systems through sound engineering and iterative simplicity
Let me be honest: whenever I spot another new business tool out in the wild, it's always the same question nagging at the back of my mind- why do we always start with a spreadsheet? I’ve seen it time and again, whether someone’s launching a business, managing
What I have seen, speaking with technical builders and seasoned business folk alike, is this: change is coming at us from both ends. Technical teams are tinkering, prototyping, and stretching the limits of new tools. The business teams – hungry for efficiency
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