Research
I contribute to the body of knowledge on context systems, memory architectures, agent operating systems, and governed AI delivery. My research bridges enterprise practice with formal frameworks — grounded in production systems, published as open scholarship.
Selected research
Current anchor papers from a broader body of work spanning books, papers, public industry writing, patents, and long-horizon teaching.
A formal lens on how systems select, transform, govern, and lower external context into executable representations that models and agents can reliably act on.
Treats agent-produced engineering work as a regulated feedback loop—explicit gates, observability, and lifecycle metrics that keep autonomy aligned with organizational control.
Broader scholarship footprint
These papers are current anchors, not the entirety of the work. The wider profile includes books, patents, graduate teaching, executive education, and public industry writing.
Books & Publications
Multiple books, papers, and public industry writing spanning machine learning, data science, private AI, and enterprise systems.
Patents & IP
Five U.S. patents across private AI, data exchange, automation, and distributed systems architecture.
Graduate & Doctorate Teaching
15+ years teaching graduate and doctoral learners across university programs and professional education.
Pluralsight / Executive Education
16 public Pluralsight courses covering AI, RAG, LLM agents, data engineering, and enterprise AI topics.
View author pageKeynotes & Industry Voice
Executive briefings, conference talks, and public industry writing on private AI, governance, and next-generation AI infrastructure.
Research themes
Recurring motifs across papers, talks, and systems work — each theme connects production constraints to conceptual clarity.
Context Compilation
A theory of how AI systems should select, transform, govern, and lower external context into executable representations for models and agents.
Cybernetic Software Delivery
A governed lifecycle for engineering work produced by autonomous agents — treating delivery as a closed-loop control system.
Agent Operating Systems
Architecture patterns for systems where AI agents are first-class participants — scheduling, resource management, inter-agent communication, and governance.
Memory Systems
How AI preserves continuity of cognition across changing data, models, tools, and experiences — from episodic memory to persistent context graphs.
Token Economics
The cost structures, optimization strategies, and economic models that determine whether enterprise AI systems are sustainable at scale.
AI Control Planes
Governance, observability, policy enforcement, and trust calibration for enterprise AI — the management layer above models and agents.
Related writing
Long-form series that unpack the same ideas in narrative, diagram-friendly form.
I am extending context compilation toward stronger invariants under shifting toolchains and model versions; tightening the measurement vocabulary for agent operating systems; and stress-testing economic models when governance and latency constraints bind at the same time.
The through-line is simple: make enterprise AI systems legible — to operators, auditors, and the models themselves — without sanding away the complexity that makes them work in production.
Interested in research collaboration? I'm available for workshops, briefings, and advisory on these topics.
Speaking & advisory