AI Behavioral Assurance: A Lean Six Sigma Methodology for the LIfecycle Governance of Large Language Models and Agentic AI Systesm

AI Behavioral Assurance establishes a process-level methodology for governing the behavioral outputs of Large Language Models and agentic AI systems in consequential decision environments. Its structural claim is that LLM and agentic systems satisfy the necessary and sufficient conditions to be treated as governable stochastic processes under Statistical Process Control, and that the Lean Six Sigma toolkit developed for manufacturing can be systematically translated to AI governance. The work adapts the five-phase DMAIC cycle (Define, Measure, Analyze, Improve, Control) to non-normal, non-stationary, unbounded, and opaque AI processes, using an Adopt, Adapt, Innovate taxonomy that classifies 40 tools and constructs by their applicability. It introduces a purpose-built measurement system, the BME (Behavioral Measurement of Entropy) Metric Suite, and a governance architecture, ALAGF (Adaptive Lifecycle Agentic Governance Framework), that embeds DMAIC as a repeatable lifecycle process. Behavioral entropy serves as the governing process variable. The methodology is mapped to ISO/IEC 42001:2023, the NIST AI Risk Management Framework 1.0, IEEE standards, and the EU AI Act, and is demonstrated through worked clinical, insurance-triage, and agentic use cases. The book targets standards bodies, governance researchers, and practitioners deploying AI in regulated environments.