Designing Governed AI Systems That Earn Trust in Production
The Trust Layer Architecture Arc
From predictive ML governance to autonomous multi-agent orchestration.
Published Architecture
Published Architecture
Active Development
Planned
Foundations → Retrieval → Agents → Orchestration
Governed RAG architecture for high-accountability compliance environments, incorporating trust boundaries, hybrid retrieval, evaluation discipline, and operational guardrails. Not a chatbot—a governed retrieval system.
View project →Governed MLOps architecture for regulated ML deployment, including fairness controls, CI/CD governance gates, model observability, and audit-ready documentation aligned to NIST AI RMF.
View project →Governed agent architecture for federal compliance workflows, instrumented against NIST AI RMF, NIST AI 600-1, OMB M-24-10, and NIST 800-53 Rev. 5. Governance artifacts — trust ledger, risk matrix, runtime decision record — checked in before a single line of agent code is written.
View project →Traditional authorization frameworks establish trust in infrastructure. AI systems introduce a second trust problem around model behavior. Defensible AI authorization requires both layers — and the governance layer has to live in architecture, not policy.
Read article →RAG is a solved retrieval problem and an open trust problem. Four silent production failure modes — and the trust architecture designed to contain them.
Read article →Most AI systems don't fail because the model is wrong. They fail because governance was never engineered into the system.
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