Archive
AI controls, agent systems, banking governance, production practice. 409 essays, newest first.
- AI Controls Architecture
Risk teams know risk. The open problem is designing controls for systems that are non-deterministic, probabilistic, and attackable in natural language.
- Governing Agents the Way Cells Govern Themselves
Six cell biology mechanisms that reveal what the networking 'control plane' metaphor misses about governing AI agents.
- The Risk Without an Engineering Solution
Every other agentic AI risk has an engineering answer. Prompt injection doesn't. That changes everything about how you design controls.
- Per-Token Pricing Is the 'Megapixels' of AI
We're optimising for the wrong number — and the history of consumer electronics suggests we'll figure this out eventually.
- Skills as Behavioral Nudges: The Lightweight Alternative to Fine-Tuning
We fine-tune models with gradient descent. We nudge agents with skill files. Same goal, radically different cost.
- The Contract Pattern: Hard Gates for AI Agents
AI agents know how to start a task. They don't always know when to stop. The contract pattern is the architectural fix.
- The Real Reason Mox Won (and What It Means for AI Transformation)
Mox didn't win because they hired better designers. They won because they had no legacy to fight. The pattern applies directly to AI transformation.
- AI Governance Category Error: Routing vs. Compliance
Your AI governance framework is a routing spreadsheet pretending to be a compliance programme. Regulators will spot the difference.
- AX: Agent Experience Is the New DX
Developer experience became a competitive moat in the API era. Agent experience is next. Most tools aren't designed for it yet.
- Don't Ask Your AI to Find Problems
Ask for bugs and you'll get bugs — whether they exist or not. Sycophancy is a design feature, and the fix isn't better prompting.
- What Makes a Great AI Consultant (Beyond Technical Skills)
The most dangerous person in an AI consulting engagement knows how the model works but has never sat in a credit committee.
- HK/APAC as an AI Hub for Financial Services: The Story Being Missed
Hong Kong has quietly run one of the most sophisticated GenAI experiments in global banking. Almost no one outside the region is paying attention.
- AI Agent Frameworks for Enterprise FS: What Actually Works vs. Hype
Most enterprise AI agent pilots in financial services fail at the same point: the second tool call. The problem isn't the framework.
- RAG for Compliance: The Hard Problem Is Chunking, Not Retrieval
Banks are deploying RAG for compliance and discovering the hard problem isn't retrieval. It's the pipeline before it.
- Banking DS to AI Consulting: What the Transition Actually Teaches You
The operational instincts built in production banking don't belong in the past. They're exactly what makes a practitioner-turned-consultant useful.
- Most Banks Don't Need an AI Strategy
The real project isn't artificial intelligence. It's the data infrastructure that AI exposes as broken.
- The Hidden Violence of Vague Instructions
LLMs aren't just tools we prompt - they're forcing functions for human linguistic evolution
- Claude Code is Not a Coding Agent
Why I use Claude Code for everything except coding: cognitive compiler for strategy, decisions, and understanding.
- Production AI vs Demos: The Intent Classification Reality Check
Building AI systems that work in the real world requires thinking beyond the demo. What actually matters when users depend on your models.
- What and Why Beat How
When implementation becomes automated, human intelligence reallocates to purpose and strategy. The cognitive hierarchy inverts.
- The Invisible Puppeteer
When algorithms shape decisions we think are ours
- Everyone Becomes Middle Management
The automation tool that creates more coordination work