Platform Foundations
Platform Foundations describe the core system patterns I use to design, govern, and scale complex platforms.
They are not tied to a single product or industry. Instead, they represent the repeatable architectural, experience, and operating principles that show up across learning systems, workforce enablement, supply chains, healthcare platforms, and commerce ecosystems.
Platform Operating Model
A decision-centric operating model for governed, enterprise-scale platforms
Enterprise AI Enablement &
Agentic Orchestration Framework
Led the design of an enterprise AI enablement and agentic orchestration framework focused on translating AI capabilities into governed, production-ready platforms. The work centered on defining how AI systems operate within real organizational constraints rather than isolated experimentation.
Established agentic workflow patterns that combined automated reasoning with explicit human-in-the-loop controls, ensuring accountability, explainability, and safe intervention across high-stakes workflows. These patterns balanced autonomy with oversight, enabling AI systems to assist decision-making without obscuring responsibility.
Defined AI operating models covering orchestration, escalation, and lifecycle governance, including when agents may act independently, when human review is required, and how decisions are logged, audited, and improved over time. Introduced observability standards to track behavior, confidence signals, drift, and operational impact.
Partnered with product, data, engineering, and compliance stakeholders to align AI enablement with platform strategy, operating models, and regulatory expectations. Emphasized reuse, consistency, and adoption through shared frameworks rather than bespoke implementations. This work positioned AI as a durable platform capability, enabling responsible autonomy at scale while preserving trust, transparency, and business accountability in regulated and enterprise environments.
Governed Data as a Product (Decision-Centric)
Led the design and execution of a governed, decision-centric data platform that treated data, decisioning, and activation as first-class product capabilities in regulated environments. The platform standardized how data is observed, learned from, and activated across regulated environments, enabling teams to build decision-driven products without duplicating governance, identity, or policy logic.
Established a unified data and decision core spanning identity resolution, consent and allowed use, signal capture, decision patterns, and auditability. This foundation enabled reusable decision services that downstream teams could safely activate across clinical workflows, member engagement, learning systems, operations, and internal tooling.
Defined operating models and guardrails that balanced autonomy with control, embedding governance, explainability, and human oversight directly into workflows and APIs. This approach supported human-in-the-loop AI, agentic decision workflows, and responsible automation while maintaining regulatory defensibility and operational trust.
Outcomes included faster product delivery, clearer ownership boundaries, reduced compliance friction, and improved adoption by treating governance, observability, and decision logic as first-class platform capabilities rather than centralized review processes. This work formed the foundation for enterprise AI enablement, agent orchestration, and scalable decision systems across multiple regulated domains.
