Senior enterprise systems architect and escalation engineer with 20+ years designing and delivering high-reliability infrastructure across regulated public sector and large-scale cloud environments. Specializes in solving technically constrained problems that intersect compliance, identity, data governance, and emerging AI systems.
Known for operating effectively in high-stakes, ambiguous environments—translating executive intent and regulatory requirements into deployable architectures that withstand real-world constraints. Currently focused on secure, auditable AI infrastructure through Technomancy Laboratories—including multi-tenant SaaS systems, agentic workflows, and identity-aware model deployment patterns.
Production memory-as-a-service for AI agents. A multi-tenant SaaS platform providing persistent, semantically searchable memory through OAuth 2.0+PKCE authentication, PostgreSQL with pgvector, and a comprehensive MCP tool suite. Designed to give AI agents reliable long-term memory without compromising tenant isolation or security.
A distributed multi-agent coordination framework built around receipt protocols for auditable workflows. Enables transparent collaboration between AI agents with verifiable execution trails, addressing the accountability gap in multi-agent systems.
A tick-synchronous terminal horror game where you are a process trapped in a memory shard, trying to reach an exit gate before the Defragmenter finds you. Not a game that uses AI — one that treats AI as a process in the same hostile system it treats a human.
Published January 2026. A framework for maintaining critical thinking and accountability when working with AI systems. Written for professionals who need to collaborate with AI without surrendering judgment. Explores AI interaction as collaborative navigation through meaning-space, where systems generate possibilities and humans remain responsible for grounding and verification.
I write about AI agent security, prompt epistemology, authority delegation in AI systems, and the structural problems in how we build and deploy AI infrastructure. My work focuses on the gaps between what AI systems do and what we assume they do.