Field notes on building AI that ships.
Practical lessons from designing and deploying AI into real operations — the unglamorous engineering that turns a demo into a production system.
Shipping Workflow One Doesn't Teach You Workflow Two
Shipping one profitable AI workflow proves you can ship that workflow, not that you have a repeatable system. This piece breaks down why the mid-year urge to scale to three agents at once is more expensive than shipping zero, and what actually transfers between workflow one and workflow two: the evaluation harness, not the code.
Read article →Your First AI Workflow Shipped. Your Second One Is Drowning in Review Cycles. Here's Why.
Your first production AI workflow succeeded because it was small enough to skip every hard organizational question. Your second one is stalling because those questions are now unavoidable, and the fix is instrumented feedback loops and a pre-agreed quality gate document, not a rebuilt governance framework. This post explains the specific failure modes (evaluation limbo, missing eval harnesses, security gaps that compound across workflows) and what to do about them before workflow two enters review.
Read article →Production AI Monitoring for Q2: Catching Silent Failures, Cost Overruns, and Quality Drift Before They Compound
You shipped the pilot. The demo worked. The stakeholders approved it in Q1, and now it's Q2 and the workflow is live in production. Congratulations. Now the real work starts. Here's what nobody tells…
Read article →Why AI Pilots Stall Before Production
The gap between a working demo and a system your team trusts on a Monday morning is where most AI projects quietly die. Here's how we close it.
Read article →Ship One Workflow, Not a Transformation
You don't need to “transform your organization with AI.” You need one four-hour task to take two minutes instead. Start there.
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