Nimitta Research

About

Nimitta Research studies how AI systems, particularly large language models and autonomous agents, should be governed in regulated, high-stakes environments. The core thesis: traditional IT governance frameworks were built for deterministic software and produce the wrong behavior when applied to probabilistic, autonomous systems. Effective AI governance requires constraints designed into the system architecture itself, not policy layered on top after deployment.

This site collects published research and documents applied governance work in healthcare and other regulated industries.

Research Focus

Constraint-based architecture for LLM and agentic AI workflows in regulated environments. AI governance frameworks that account for autonomy, non-determinism, and continuous behavior rather than fixed, auditable logic. The gap between conventional IT governance models and what AI systems actually require to be safely and effectively deployed at scale.

Published Research

"Governing AI Agents in Healthcare: A Constraint-Based Architecture for LLM Workflows"

Working Paper, SSRN, 2026.

"The Governance Mismatch: Why Traditional IT Governance Produces the Behavior It Is Supposed to Prevent"

Working Paper, SSRN, 2026.

Applied Work



Beyond research, this work is applied directly with organizations deploying AI in regulated environments. Recent engagements include setting AI governance policy for healthcare and technology clients deploying LLMs, converting ungoverned pilots into compliant, board-defensible programs, and advising on cloud migration and infrastructure modernization initiatives that build governance in from the start rather than retrofitting it.