AI Reliability: Shift Harness's collection on Developing AI Products. Expect field-tested guidance on AI QA testing, written for CTOs and delivery teams.
AI Strategy
Ten steps that each work 95 percent of the time succeed together only about 60 percent of the time. Reliability compounds, and every agent handoff is another multiplier below one. The handoff, not the agent, is the unit of risk.
Quality Harness Engineering
There is a number most CTOs cannot explain. Output volume is up, adoption is healthy, and the delivery metric that was supposed to move has not. The productivity did not disappear. It relocated.
Role Playbooks
Test count is up, the suite runs in minutes, coverage climbed after the AI generators went in. And escaped defects have not moved, or drifted the wrong way. More tests, same protection.
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.
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.
AI Strategy
MCP is dead. A2A is the future. Everything should be a workflow. Most of these debates are category errors. The tools are not competing. They manage different constraints, and the real decision is which trade-offs your organization can afford.
AI Strategy
The eval set is not a quality artifact. It is the operative specification of an AI product. Whoever curates the failure cases makes the product decisions, regardless of what the PRD says.
AI Strategy
The demo works and the eval suite passes. Then production is always one more sprint away - because every AI fix breaks something else. That pattern has structure, and it is not about the model.
AI Strategy
Cost becomes a product problem before most teams notice the shift. By the time someone runs the unit economics on an AI feature, the design decisions that drove them are already shipped.
AI Strategy
Every prototype demoed beautifully. Six months later nothing has shipped, and "our AI strategy is basically a list of pilots" stops being a confession and starts being a diagnosis. Here are the eight stages that move a prototype into production.