Everything we publish lives in the blog. This page is the index into it — organised by the argument the article advances rather than by the date it appeared, so you can enter at the point where your own question begins.
Strategy, semantics, engineering, governance, learning.
CIOs, CDOs, L&D leaders and architects who have to choose this quarter.
Written from Singapore and Hong Kong, referenced globally.
Most readers arrive with one of four situations. Each path below reads in order and ends at a specific article rather than at a general reading list.
Start with the working definition of an agentic business and the three conditions that separate it from automation with a chatbot attached.
Path 02A scoring method for candidate processes, the autonomy tiers, three scenarios to avoid, and a 90-day plan finance will accept.
Path 03The three ways RAG fails, a ten-parameter tuning checklist showing what each one moves, and when GraphRAG earns its cost.
Path 04Why three disconnected systems drift apart, and how to sequence a single capability substrate underneath people, partners and digital employees.
Our own coverage map. If a topic is in a pale column we will say so rather than pretending to expertise we do not have — which is also why this page is not a list of everything anyone has ever published about AI.
| Agentic business | Agentic operations | Ontology & data | Context engineering | Governance | Enterprise learning | |
|---|---|---|---|---|---|---|
| Definition and framing | ||||||
| Comparison and selection | ||||||
| Implementation guidance | ||||||
| Governance and evidence | ||||||
| Workforce and skills | ||||||
| Regional focus |
We write to be useful to somebody who has to make a decision. We do not write to be quoted in a press release, and we will disagree with a prevailing position when we think it is wrong.
The full text lives in the blog. Filter here to see which article advances which part of the argument.
A definition built on three necessary conditions — shared business semantics, bounded authority, measurable output — and the six layered terms that get confused with each other every week. The argument the rest of our writing rests on.
A six-dimension scoring method for candidate processes, the autonomy tier table, three scenarios to avoid, and a 90-day plan that produces a number a finance function will accept.
How enterprise ontology differs from a glossary, a data model and a knowledge graph — and why the tenth use case costs a fraction of the first. With the six semantic layers and the reuse economics.
The three ways retrieval fails in production, a ten-parameter tuning checklist with what happens when you move each one, and an honest assessment of when GraphRAG earns its cost — and when it does not.
ISO 42001 against ISO 27001, NIST AI RMF and the EU AI Act, plus the governance problems traditional IT controls do not cover — autonomy, tool calls, cross-system action and unpredictable output.
Each system owns a different question. Without one employee record underneath, they drift — and adding an AI layer on top of three disconnected systems makes it worse. How to sequence it properly.
These topics are one argument, not five. An agentic business without shared semantics is a demo. Semantics without governance is an incident. Governance without capability in the people and the agents who must operate it is a document. Read them in the order your problem presents itself — or subscribe to the monthly briefing and we will send each one as it publishes.
Each topic links to the practice or platform that delivers it, so reading turns into a conversation rather than a dead end.
Operating models, work decomposition, authority allocation and digital workforce strategy.
Business semantics, semantic layers, knowledge graphs and enterprise data engineering.
Retrieval, context budgets, tool contracts, agent architecture and evaluation harnesses.
ISO 42001, risk classification, autonomy tiers, control testing and audit evidence.
Capability for employees, external partners and digital employees as one system.
Four editorial rules. They are why this page reads like a practitioner wrote it rather than a content team.
Every piece comes from something we built, measured or had to fix. No thought leadership written by people who have not run an engagement.
Each one is written for a specific role making a specific decision. If it does not help anyone decide anything, we do not publish it.
Definition-first, question-form headings, comparison tables and a stated position — so it can be found and cited, not just read.
Articles carry dates because the field moves. Anything that has been materially wrong gets corrected visibly rather than quietly.
Every topic below is covered in the sections above, and each links to the page that goes deeper on it.
Take one question from these articles into a working session with the practice lead who wrote it. Forty-five minutes, and you will have a point of view on your own situation.