How The Governance
System Emerged.
The intellectual evolution mapping the progression from foundational AI unit economics up to deterministic runtime enforcement.
Recent Published Works & Laboratory Briefings
Most Companies Shouldn’t Be Using Autonomous Coding Agents Yet ↗
The technology is getting ahead of the environments we are putting it in. Autonomous coding agents operating in shared environments create investigation and cleanup bottlenecks that erase productivity. Before increasing agent autonomy, engineering teams must establish strict boundary controls, autonomous verification loops, and failure recovery harnesses.
The AI Coding Tool Battle Is Moving Somewhere More Important Than Code ↗
As foundation models become hot-swappable commodities (exemplified by GitHub retiring six older Copilot models), developer tool competition shifts to the surrounding execution harness. The true economic value of an AI coding platform is defined by environment pre-provisioning, recovery mechanisms, and making failure cheap rather than raw autocomplete benchmark velocity.
How Does Meta’s Muse Code Compare to Other AI Coding Tools? ↗
Evaluating Meta Muse Code against Cursor, Claude Code, and Google Antigravity reveals that multi-agent concurrency breaks down at the runtime layer. While Git worktrees isolate file diffs, systems still collide on shared port bindings, database transaction locks, and environment state. Developer ROI is maximized not by autocomplete speed, but by autonomous verification loops and making failure cheap to roll back.
How Context Engines Power AI Career Intelligence ↗
Stateless prompt wrappers fail in career workflows due to context loss and lack of persistent memory. CareerWin.ai implements structured context schemas, metadata preservation, and relational database state to replace static PDF resumes with dynamic career operating systems.
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.
Economics
Distilling the unit economics of LLM inference, indexing raw engineering throughput, and auditing R&D capital allocation.
Governance
Establishing the Product Debt Index (PDI) to convert undocumented technical debt into boardroom-ready exit valuation metrics.
Operational AI
Solving the Cost of Predictivity. Modeling AI margin collapse points, cloud FinOps repatriation breakevens, and small model alternatives.
Agent Security
Identifying security liabilities in autonomous systems. Mapping jailbreaks, shadow AI data leaks, and sandbox evasion vectors.
Runtime Governance
Shifting from passive observability to deterministic physical control boundaries. Building state-verification engines.
Exogram
Deployment of the sovereign Exogram runtime interceptor. The physical proxy layer enforcing zero-trust governance.
Ecosystem Alignment Map
Every publication, tool, and software system mapped back to the core research program.
The Production AI Governance Architecture
Every resource on this site is a node in a single multi-year research program exploring AI operational limits.
Frequently Asked Questions
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