Notes
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- Approvals and control for AI agentsHow to design human in the loop approvals, permissions and audit trails for AI agents, so they can act in your systems without losing control.
- Why AI agents fail in productionWhy AI agents that work in a demo fail in production: no agreed standard, a missing model of the business, too much access, no owner. How to fix each one.
- Agentic vs generative AI: what’s the difference?Generative AI produces content when asked. Agentic AI pursues a goal over several steps, using tools in your systems. The difference, and agents vs chatbots.
- Forward deployed AI companies in 2026Who offers forward deployed AI engineers in 2026: model labs, cloud and software vendors, platform companies and consultancies. Sourced, checked 11 Oct 2026.
- Forward deployed engineer vs consultantA forward deployed engineer builds and runs the system inside your company. A consultant recommends. A solutions engineer helps you buy. Which you need.
- How to build an ontology for AI agentsHow to build an ontology for AI agents in seven steps: start from one piece of work, name the kinds of things, their sources, relationships and allowed actions.
- How to measure the ROI of AI agentsMeasure AI agent ROI per piece of work: baseline quality, cost, time and effort, track cost per accepted task, then stop or scale. With a worked example.
- Why do AI agents give confident wrong answers?AI agents give confident wrong answers because they get your tools but not the map of your business. Why prompts and more tools fall short, and the fix.
- How to deploy AI agents in an enterpriseHow to deploy AI agents in an enterprise in seven steps: start from one piece of work, audit it on site, redesign it, agree the standard, then build and run it.
- Map first, tools secondAI agents get tools built for people and rebuild a map of the company on every run. A world model for knowledge work gives them the map; the tools feed it.
- World model vs knowledge graph: the differenceA knowledge graph is the structure. A world model for knowledge work adds time, sources and a line between accepted facts and guesses. How the two differ.
- What is a context graph for AI agents?A context graph for AI agents is a connected record of what one task needs: the things involved, their current state and the decisions behind them, sourced.
- What the EU AI Act asks of B2B companiesThe EU AI Act asks little of the AI work that creates most value and a lot of AI that decides about people. The dates, the tiers and what we would do now.
- One week at one companyAI agents for sales, a week after the first call: a list that refreshes itself, meeting prep before every call and a territory model new reps can ask.
- The case for GraphRAGGraphRAG builds a connected view of your documents once and reuses it, to answer questions no single document answers, more fully than plain retrieval.
- Agree the standard before you buildBefore you build an AI agent, agree the standard on real examples: quality, cost, time and human effort. The pilots that die never agreed what good meant.
- Redesign the work before you automate itAn agent on the current process makes it faster, waste included. So the first two weeks of a deployment redesign the work, and only then do we build.
- More context makes agents worsePast a point, adding context degrades an agent. It needs the smallest sufficient view of the work, and only a model of the company can decide what that view is.
- Why coding agents work and company agents don'tCoding agents took off because a repository is already a world an agent can stand in. A company is not, so you build that world before the agent can work.
- The model can be rented. Your world cannot.Every company rents the same AI models at a falling price. The advantage that lasts is a maintained model of your own business for them to work in.
- What sight meansConnected facts show what no single source contains. That is the real payoff of a world model, and the larger bet we are making.
- What is a world model for knowledge work?A world model for knowledge work is a sourced, current map of a company and its market that AI agents query instead of guessing. Its three layers and an example.
- What is an ontology for AI agents?An ontology defines what exists in a business, what it means, how things relate and what can be done, so AI agents can act on it. With a sales example.
- Access is not a mapConnecting an agent to the CRM, Slack and email gives it territory. The map, what those things are and how they relate, still has to be built.
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