Making AI workflows observable, reliable, and trustworthy.

I work on the reliability layer between AI agents, workflows, and human operators. My work sits at the intersection of agent observability, workflow debugging, human-in-the-loop systems, and reliable AI operations.

I help founders and teams understand, debug, and operate AI workflows reliably. That means tracing what happened, explaining why it failed, and designing the feedback loops that keep production systems under control.

I write reliability essays, failure analyses, case studies, and reports about the production gap: the distance between an impressive demo and an AI workflow that holds up under real users, cost pressure, and operational risk.

This site is my working record of those patterns: observability that supports debugging, human-in-the-loop systems that improve operations, and reliability architecture that treats AI workflows as systems to be operated, not magic to be trusted.

I am building toward one simple idea: reliability is the missing layer of AI.