Most enterprise AI initiatives fail at the org boundary, not the model. The interesting work is in process, capability, and goal translation— not the model.
Fourframeworks I’ve put my name on.
Each came out of a real engagement, then got written down because it kept being useful in the next one. None of these are decks. They’re decisions you can run.
- F-01DECISION FRAMEWORK · 2026
Organizational Capability Map
Which workflows agents should own, which they should assist, and which they should be kept out of.
- F-02ARCHITECTURE PATTERN · 2026
Goal Translation Infrastructure
The translation layer between organizational OKRs and agent reward functions. Without it, agents optimize for the wrong thing precisely.
- F-03SPECIFICATION FORMAT · 2026
Agent Actionable Objectives
A standard shape for goals that an agent can actually act on — measurable, bounded, owner-attributed.
- F-04DIAGNOSTIC CONCEPT · 2026
The Intent Gap
The measurable distance between what an agent optimizes for and what the organization deploying it actually needs.
The arguments, in long form.
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- 6 MIN
The Scale Doesn't Matter, The Mistake Is the Same
- 5 MIN
Where Do I Even Start? (The Wrong First Question)
"Where do I even start with AI?" is the wrong first question. It skips a step that determines whether everything after it works or wastes your time. The right sequence: understand the technology, pilot with measurement, then evaluate which processes should look entirely different.
- 5 MIN
Your AI Agent Shouldn't Have Your Employee's Job Description
When teams "agentify" an existing process, they hand the agent a workflow designed around human constraints. The agent inherits every stop-and-wait point, every workaround, every approval layer it doesn't need. The fix isn't to optimize the agent. It's to redesign the process.
- 10 MIN
What I Learned Building a Document Format from Scratch
I started thinking the hard problem was document parsing. Then format design. Then sync logic. The real challenge was building at the interface between two domains that don't talk to each other.
- 9 MIN
The Design Decisions Behind an AI-Native Document Format
Processing a Word document through raw OOXML costs 359,706 prompt tokens. Clean markdown costs 106. Five design decisions that make that gap useful without losing formatting.
- 7 MIN
Why AI Document Workflows Are Broken
Every AI tool that works with documents treats formatting as disposable. For organizations running hundreds of documents through AI editing pipelines, it's a structural cost nobody is tracking.
- 11 MIN
Goal Translation Infrastructure: Encoding What Your Organization Actually Wants
Your Capability Map says a workflow is Agent Ready. Now what does the agent optimize for? OKRs were designed for humans. Agent Actionable Objectives are the translation layer that's been missing.
- 11 MIN
The Organizational Capability Map: Deciding What Your AI Agents Should Actually Own
Most AI deployments fail not because the technology doesn't work, but because nobody decided which workflows agents should actually own. The Organizational Capability Map is a framework for making that call.
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