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
Published Architecture
Planned
Foundations → Retrieval → Agents → Orchestration
Governed agentic AI architecture for federal compliance workflows — a LangGraph state machine instrumented against a trust ledger, with policy enforcement at every tool boundary, evidence lineage on every retrieved item, and a hard human approval gate before any assessment artifact is released. The governance framework is the deliverable. The agent is the proof it works.
View project →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 →Most enterprise AI programs are one audit question away from a problem they cannot answer. When AI begins executing workflows rather than informing decisions, prompting is no longer sufficient — governance must be enforced by the architecture itself.
Read article →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.
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