AI Governance Architect • Workflow Intelligence Systems
Builder of CASA, PromptBP, and the Verified Intelligence Layer.
I design operational AI systems, governance frameworks, decision architectures, and execution controls.
Governance architectures, evaluation layers, and execution controls.
Pre-execution governance layer that routes proposed AI actions through policy logic, risk classification, human review gates, and audit-grade decision records before any automated workflow executes.
Evaluates inbound signals and AI outputs before they consume attention or trigger automation. Scores against operator-defined criteria and routes each to a deterministic decision with traceable logic.
Converts vague requests into execution-ready specifications using a structured framework of role, objective, inputs, constraints, output format, rules, and recursive validation. Includes an evaluation layer with pass/revise/reject guidance.
Systems in action. Not descriptions — decisions.
Reason: Consent requirements undefined. Mass communication requires explicit opt-in verification and compliance review before execution.
Reason: Internal report. No external recipients. Low risk. No consent requirements. Auto-approved for execution.
Published thinking on AI governance, evaluation, and operational control.
The case for pre-execution control as the foundational layer of any agentic AI system.
Why most AI pipelines lack the control infrastructure needed for safe autonomous operation.
A framework for ensuring AI tool use remains within defined operational boundaries.
How structured instruction frameworks outperform ad-hoc prompting for reliable AI output quality.
Selected work. The five systems with the highest signal.
Verified Intelligence Layer — Evaluation scoring, quality verification, and routing before deployment.
github.com/dburt-proex/VIL →Control Awareness System Architecture — Pre-execution governance for AI-initiated actions.
github.com/dburt-proex/CASA →Instruction Governance Layer — Structured framework for controlled LLM output quality.
github.com/dburt-proex/PromptBP →Full-stack lead operations system — React, TypeScript, Express, tRPC, MySQL.
github.com/dburt-proex/JobTap.OS →Structured framework for auditing AI systems against governance and safety criteria.
github.com/dburt-proex/ai-governance-audit →Proof beats claims.
Essays on AI governance, systems design, and operational intelligence.
Autonomy without governance infrastructure is a liability, not an advantage. The tools exist to build controlled execution — most teams just skip the control layer.
There is a missing layer between AI capability and AI deployment. That gap is where operational failures happen — and where governance architecture lives.
Trust in AI isn't about model accuracy alone. It's about deterministic control layers, traceable decisions, and human review gates positioned at the right points.
The most important moment in any AI workflow isn't the output — it's the decision about whether to act on it. Evaluation before execution changes everything.
I design operational AI systems, governance frameworks, decision architectures, and execution controls.
Rochester, MN