Runtime Governance
The final integration layer where frameworks, diagnostics, and educational guidelines compile into deterministic physical control boundaries.
Contextual Boundary
Why This Exists
Most AI discussions focus on model capabilities. My work focuses on what happens after deployment. As AI systems become embedded in products, organizations face a new class of problems involving economics, governance, security, reliability, and operational control. The Production AI Governance Framework exists to help organizations understand, measure, and manage those challenges.
Core Analytical Axioms
Forensically proven concepts in this operational boundary.
Deterministic Execution Sandbox
An execution boundary that forces probabilistic outputs to comply with explicit state logic rules before shipping.
Guardrails rely on "soft" LLM filters that are bypassed by adversarial prompts or simply fail randomly in production.
Guarantees absolute conformity, preventing rogue model responses in high-trust applications.
State-Hashing Audit
A cryptographic commit structure that records every agent action on a tamper-proof ledger.
Autonomous agents take actions (sending emails, modifying records) without deterministic audit trails, creating diagnostic blindspots when errors occur.
Guarantees total auditability for regulatory compliance and error trace recovery.
Rule-Based Interceptor
A low-latency physical proxy intercepting LLM input/output streams to enforce security rules at the network layer.
Validations written inside software code are easily bypassed, slow down processing, and are difficult to update.
Enforces site-wide data-leakage and agent budget limits at the proxy tier, bypassing database delays.
Want to apply this to your organization?
Run a free diagnostic first. If the numbers concern you, book a session to build a remediation plan.
Richard Ewing — AI Economist & Capital Auditor