AI operating model
An agent shipped clean code that quietly broke a convention living in a wiki page it never opened. The instruction file is where a team's standards reach the code, or quietly fail to.
AI operating model
Every AI coding session feels productive. Then the quarter ends, delivery is flat, and the board asks what the per-seat spend bought. Both facts are true at once, and the gap between them is the cost you keep re-buying.
AI Security
Agent security debates fixate on attacks reaching the agent. The question that comes first: what is the agent allowed to do, and through whose identity? That standing authority is owned by no one and revisited approximately never.
AI Strategy
Most AI products do not stall for lack of features. They stall because they ask users to build the machine before it does anything, then never check whether anyone comes back. The fix is an outcome-first shape: one valuable result, proven.
AI governance
You funded the AI rollout. Per-developer output climbed, the demos got faster, and then the delivery numbers refused to move. The slowdown most teams blame on governance is usually the wrong governance, or its absence. Here is the version of the argument that holds up.
AI Strategy
A passing eval suite feels like coverage. It is coverage of the world on the day you wrote it, and that world has already moved. What keeps the suite honest is a renewal loop, not a better set written once.
Quality Harness Engineering
An agent applies the standard it can read, not the one you wrote. Writing a convention into the files the agent loads raises the odds it follows it, but guidance is not enforcement. The fix is a deliberate split across three control layers.
AI in Software Delivery
AI output volume is up and the dashboard says adoption works. Escaped defects and rework say otherwise. The gap is a standard your team never rewrote: what "done" certifies once an agent wrote the code.
Quality Harness Engineering
Output is up but the metric has not moved. The reflex is to add a standup. Often the stall is an under-defined result, not a coordination gap. The shape of the stall tells you which lever to pull, and they are not interchangeable.
AI Measurement
Self-report drifts upward and license counts measure procurement, not practice. The signal that holds up is the artifact each role produces every week, read on a ladder from exists to outcome.
Quality Harness Engineering
Skills, subagents, hooks, MCP. Most leaders treat them as one pile of jargon and let whoever wired the agent that morning decide. Each answers a different question, and the question is yours.
Quality Harness Engineering
The model writes the function in four seconds and the team still does not move. AI removed the cost of writing code and left the cost of specifying, governing, reviewing, and remembering it untouched. Six artifact classes are what close that gap.