Writing archive
Older essays on agentic systems, governance, operations, incentives, and the work of making AI useful in real organisations.
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
Here’s the question that keeps landing on my desk: How can AI support the people whose jobs feel under threat? I keep hearing from managers and teams worried that AI is coming for roles, not to help but to hover overhead and monitor. I get it. If you introduce
If you’re building agentic workers, you’re probably drowning in data and none of it feels quite right to keep. Storing every scrap of operational noise isn’t just expensive and messy, it crams your agent’s mind full of useless clutter. Humans don’t do this. We
Every robust AI system I’ve built – and every fragile one, too – has one thing in common: the foundation is everything. I want to lay out why we always start simple, how you check what’s happening in your agentic system, and the real hazards of leaping into co
Because when we're working with Agentic AI, one of the best methods is to start working with good data and system design. Think about how, well, aroused that your system is gonna run on. How will you know when something starts? How are we know when there's som
Because amidst the excitement about agentic AI, a subtle but persistent challenge keeps cropping up. It’s not about better tools, sharper reasoning, or the intelligence of the agents themselves. It’s about how these systems decide what is actually worth rememb
A painful nail infection turned into a leadership lesson on decision quality: why confident crowd advice can be riskier than careful AI, and how to build an escalation mindset that keeps people safe.
Why leaders must stop treating AI as a magic box, and start running agentic workflows like a real team before the next Deloitte-style scandal lands on their desk.
How JARVIS-style agentic crews and conversational AI are turning week-long projects into six-hour workflows for real teams.
A leadership-level reflection on the long arc of AI, from Turing and sci‑fi to world models and governance, and what it means for how you lead now.
Why traditional hierarchies are breaking, how agentic systems enable outcome-based
If your documentation lives everywhere and nowhere, your AI agents will fail. Here is how leaders can fix the chaos before it kills automation and onboarding.
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