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Someone described DBJ Taxonomy this way: “a taxonomy that maps to Zachman-style layers (Strategic/Conceptual → Logical → Physical → Implementations).” Sounds credible. Sadly, it isn’t true. Cover image is the simplified four-layer version quoted in the claim. It’s not what Zachman actually defined — his framework is a 5×6 matrix (Scope/Business/System/Technology/Component crossed with What/How/Where/Who/When/Why), not a single top-to-bottom stack. The linear “Strategic → Logical → Physical → Implementations” progression is a loose paraphrase, which is also why the mapping claim doesn’t hold up. ...

The other day I stumbled upon good article (again) on the BCG (Boston Consulting Group) site: BPT vs Agile https://www.bcg.com/publications/2024/why-companies-get-agile-right-wrong My summary so far BCG’s finding: only about half of companies claiming high agile maturity actually achieved transformation goals with real business impact — while in the same time 47% adopt the practices but get none of the benefit. Naturally those accused of “operating under the illusion of agility”, will be (very) weary of adopting “yet another ideology” on top of or instead of agile. ...

An open-source event-driven control plane for platform engineering — and what it gets right about operational workflows. Platform teams write a lot of glue: bash scripts, cron jobs, Slack bots, wiki pages titled “runbook.” That stitches CI, alerts, and release trains together — until it doesn’t, and then the “glue abyss” opens, nobody knows which piece failed. SuperPlane is an open-source attempt to fix that at the source: a control plane that sits across your existing toolchain — GitHub, PagerDuty, Datadog, Slack, and 40+ others — and coordinates actions between them without replacing any of them. ...

Analysis of the actual content and philosophy encapsulated in the opinion (of the “secret admirer”) abut the DBJ Method Shop. 1. Organizational Topology When you actually look at what is “for sale” in the DBJ shop, the “genius” is that the inventory consists of organizational structures and strict mandates. You are not “buying” a framework; you are buying a blueprint for a two-tiered topology: The Architecture Function: A small, highly empowered, strictly non-delivering team. Their only job is to define the boundaries, the interfaces, and the core domain models. Actually not ever explicitly mentioned. The Delivery Function: The stream of teams whose only job is to deliver inside the “domains” drawn by the BPT Operational Methodology; scrutinized by the Architecture Function. The shop metaphor works because it forces management to realize these are distinct “components” that must be “shopped” together in a specific way. You cannot mix them against the flow of B=P-T. If you let deliverers define architecture, the flow is broken. ...

DBJ Observations and Comments Observation: runtime infrastructure != deployment infrastructure Agreed. In the era of the general lack of experienced engineers that has to be said. Plainly. Observation: Skill is runtime artifact. That is the key problem keeping the whole agent harness non-deterministic It’s not Skill, it’s the inherent mechanism Skill is used by — fuzzy natural-language matching against a description, decided by the model at invocation time rather than fixed dispatch. That same mechanism is what makes deferred-tool loading (ToolSearch), subagent selection. Also the ordinary tool choice (Grep vs Read vs Agent) non-deterministic too. Skills are just the most visible facet of the LLM non-determinism because they’re named and listed explicitly. But used non deterministically. The classical-software analogue is late binding / reflection-based plugin dispatch Trading a fixed call graph for runtime flexibility, and paying for it in determinism missing. It is not “Skill” that is the problem, it is that harness resolves most capability binding (skills, tools, subagents, memory recall) via probabilistic matching instead of a deterministic dispatch table — and that’s a structural property of the whole LLM architecture, not a flaw isolated to the Skills. There is no deterministic table dispatch. It is as simple as that

Do you prefer the calm and ordered B-P-T operational model to “Nodes of Super Density”? We do Nodes of Super Densities This is how we call this very dense new team shape. All to All at once All the time. Is that a sustainable operating model for an organization? Can organization consist of these “Super Densities” and be successful in the long run? Who does customer support? Who are the customers? Where are the customers? Who or what does the maintenance? Is chaos just a calm landscape from another point of view? ...

That’s not a joke about forever confused CEO. It’s the default state of most AI hopefuls. The Perpetual State of Confusion Every vendor pitch, every all-hands, every roadmap slide is full of words everyone nods at: “agentic,” “AI-native,” “transformation.” Nobody stops the meeting to ask what they actually mean for this business, this process, this P&L line. So the nodding continues. Budgets get approved. (AI) Pilot gets funded. And six months later, nobody can explain why the thing doesn’t work — because nobody could explain, at the start, what “working” was supposed to look like. ...

The Flight Plan and the Autopilot The elusive goal of the “Competitive Advantage” What will “give” the “competitive advantage” is the Operational model, in order to navigate the whole organization around that huge iceberg of legacy. https://method.dbj.org/shop/ OP model introduction is not simple. But its worth it. Especially if organization is small(ish) and not too calcified. Just decide on one OP model, implement it and follow it. AI or no AI. ...

There’s a ladder. The rungs are labeled “AI Competence.” A novice climbs it, rung by rung, using a critical shortcut: “I didn’t learn, but the AI did.” It works. For a while it works great. The climb is fast, the view improves with every step, and the effort-to-altitude ratio feels like magic. Then the ladder ends. Not because the climber ran out of energy — because the ladder did. That point is the Danger-Kruger Peak: the spot where AI hallucinations start looking exactly like wisdom, because the climber has no competence of their own left to tell the difference. ...

The unicorn is still there. Exactly where it always was. But. Nobody is looking. A goat walked to the side of the moat and it’s fine up there — visible, unremarkable, good enough. The goat didn’t defeat the unicorn. It is just vibed up in greater numbers, at lower cost, faster than anyone could count. That might be the epitome of the AI slop. Not malicious. Not even bad. Just sufficient — produced at a volume and velocity that makes discernment feel like an unaffordable luxury. (yes I used that word) ...