AI in Software Delivery tracks AI Enablement of Delivery Teams. Browse the hub for ai developer tools, aimed at CTOs and delivery teams putting AI into real work.
Quality Harness Engineering
The dashboards look like a win: PRs merged up, cycle time down, adoption climbing. Then the same leaders say delivery feels flat and quality feels thinner. That gap is the subject.
Quality Harness Engineering
Your engineers ship more code than ever, adoption is high, and delivery has barely moved. The instinct is to read that as an adoption problem. It is an accounting one.
AI operating model
An engineer will drop the phrase into a planning meeting and mean it as a personal workflow upgrade. Take the phrase seriously. The framing it arrives in is the part to reject.
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 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.
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.