AI Agent Reliability
Powerful models make getting output that has the appearance of quality from agents easy. But incoherence and systemic mistakes make it hard to trust what they produce. I measure where agents break and build the gates that catch it, so deploying them becomes a decision, not a gamble.
You can only trust what you can verify. The unit you verify against is the verification surface. Reliability is a property of that surface, not the model you happened to pick.
The framework the rest builds on, measured across 5,109 gate checks over 97 days. Where agents fail, and the gates that catch it.
All the research →One of my tools: an open-source ML architecture lab in Go, pulled over 7,000+ times on Docker Hub.
I love hearing about challenging, complex problems and how people approach them. If your team is wrestling with this, I'm always open to a conversation.