Technical Blog
Deep dives into AI engineering, the CAT framework, mathematical foundations of ML systems, and lessons from production deployments.
Reliability Testing for LLM-Based Systems
The CAT Framework White Paper
Comprehensive framework for conducting reliability tests on LLM systems using validators, verifiers, and reliability tensors. Includes binomial experiments, generative conditional validators, and production monitoring strategies.
Agentic Architecture
Structuring Agent-Tool-User Interactions
A structured approach to modeling interactions between agents and tools through chat histories, cycles, and graph-theoretic representations. Foundational concepts for understanding agentic AI systems.
Formalized Structures: The Algebra of Agentic Architectures
Mathematical Formalization of Tool-Using AI Systems
Mathematical formalization of agentic AI through tool call matrices, selection masks, and tensor operations. Defines the algebra underlying agent-tool interactions with formal propositions and proofs.
From Theory to Production
Building an Enterprise-Grade AI Testing Framework
Transform mathematical elegance into production systems. Comprehensive guide to building testing frameworks that power Fortune 500 AI deployments.
The Mathematics of Trust
Graph Theory and Bayesian Analysis for AI Reliability
Mathematical foundations powering AI reliability. Explore graph-theoretic models, Bayesian frameworks, and tensor analysis that make agentic systems provably reliable.
Redefining Trust: Agentic Reliability Testing
Pioneering Reliability in Agentic Systems
Groundbreaking work on agentic reliability testing—a mathematical framework that's changing how we think about AI system validation. From one of only eight official OpenAI partners worldwide.