From AI pilot to production: the operating system you need
A practical framework for moving promising AI experiments into accountable, durable operations.
Field notes on putting AI into production, designing accountable workflows, and creating measurable operating leverage.
The latest agent systems can act for longer and across more tools. The winning architecture is not maximum autonomy—it is controlled, observable progress.
A practical framework for moving promising AI experiments into accountable, durable operations.
Inference is only one line in the AI cost model. A better business case measures the complete operating loop—and the exceptions it creates.
New transparency rules sharpen an old lesson: responsible AI becomes real through interfaces, evidence, controls, and ownership.
How to place human judgment where it creates confidence without turning automation into another queue.
As agents absorb more execution work, understanding the operating problem—not merely prompting the model—becomes the scarce capability.
Why connecting decisions, exceptions, and feedback matters more than automating isolated tasks.
A concise note when we publish something worth your time.