Work

Authority Archive

Selected work where comprehension and verification were the difference between an AI system that shipped and one that stalled.

Selected architecture work, case studies, and deployed systems. All enterprise engagements are anonymized. Metrics are real.

Lead Case

ENTERPRISE · AI-POWERED ARCHITECTURE COMPREHENSION & IMPACT ANALYSIS

See how the system fits together — before you change it.

A governed knowledge layer that makes a 500-developer codebase legible — and the cost of a change visible before the change is made.

For a Fortune 500 engineering organization, I designed an AI-powered comprehension and impact-analysis platform: a governed, curated knowledge layer that lets architects and product managers see how a large application fits together and trace the downstream effect of a change before they build it.

  • A curated knowledge and relationship-graph layer spanning a ~500-developer enterprise platform
  • A Model Context Protocol read-tool interface returning discovery guidance, downstream-impact hints, and next-best actions
  • A readiness-gated rollout — named owners, approved decision records, and acceptance evidence before any production advance
“Most enterprise AI fails because teams can't see what they've built. This made the system legible — and made the cost of a change visible before the change was made.”
Blast Radius Mapping Architectural Intentionality
The Method

The Comprehension Architecture Framework

The method beneath the Standard. The Comprehension Architecture Framework is five disciplines that turn AI you hope works into AI you can stand behind — the proof of rigor underneath the promise.

Failure Mode Engineering

Designing for the path where it doesn't go right — before the happy path is even written.

Blast Radius Mapping

Knowing exactly who gets hurt when a system fails: how badly, how fast, and how long before anyone notices.

AI Override Discipline

A formalized practice for catching the model when it's wrong — the discipline almost no one has.

Evaluation-First Architecture

Building the evaluation system before the AI system, so you ship changes you can prove are improvements.

Architectural Intentionality

Judgment made visible: what you considered, what you rejected, and why — not whatever the AI produced.

The Comprehension Audit scores you against these same five disciplines.

Fortune-500 AI program delivery and hands-on autonomous-agent architecture at production scale — almost no one in the market has both.

Enterprise

AI-Driven SDLC Transformation

Led AI-driven software development lifecycle transformation at a Big 4 professional services firm, deploying production AI tools across a 500+ developer program and achieving 86% task reduction in code review cycles.

Coding-Standards Enforcement — In-IDE and CI/CD

Built an AI-powered coding-standards enforcement system that connects an organization's standards repository to developer workflows in both the IDE and CI/CD, via Langflow (AI workflow orchestration) and the Model Context Protocol. In-IDE, it generates standards-compliant code and answers standards questions in chat; in CI/CD, it automatically reviews pull requests and posts inline comments on violations. Ships built-in rule sets across Python, C#, TypeScript, and JavaScript, with a GitHub-direct backend and a planned semantic-search backend (Azure AI Search) for large repositories — turning written standards no one reads into automated, enforced guardrails at the point of work.

Evaluation-First Architecture

Governed Low-Code Enterprise AI-Agent Accelerator

Designed a low-code enterprise AI-agent accelerator on Microsoft Power Platform — Copilot Studio agents, Power Automate orchestration, and Dataverse-backed long-term memory — that emulates Semantic Kernel orchestration and planning patterns while inheriting Power Platform's enterprise governance (DLP, RBAC, compliance). Integrated Azure OpenAI and Azure AI Search for semantic capability and memory retrieval, with an extensible plugin system and multi-agent coordination across domain-specific agents over shared context — packaged as reusable templates so business units can stand up governed AI agents without extensive custom development.

Independent / Frontier
LIVE FLAGSHIP
2025–Present

Multi-Agent Operating System

Multi-Agent Systems · AI Architecture

Architected and deployed a multi-agent AI governance framework with 20+ specialized agents classified across four architectural tiers — knowledge counsel, evaluation & verification, autonomous execution, and architectural governance — each with defined scope boundaries, inter-agent handoff protocols, and a commission-based task routing model.

20+ specialized agents 4 architectural tiers Zero scope bleed between agents
Architectural Intentionality
Read the architecture →
LIVE · PROOF OF WORK FLAGSHIP
2025–Present

Comprehension Audit — Production AI Evaluation

AI Comprehension · Eval & Maturity

A four-question, ten-minute diagnostic that scores enterprise AI project comprehension across 8 weighted dimensions — clarity of purpose, architectural intent, failure-mode awareness, and AI-override evidence. Returns an L1–L5 maturity band and a per-dimension report. No multiple choice.

Live diagnostic 8 dimensions L1–L5 maturity
Take the audit Open-source implementation →

Auditable Trace-Tree Observability

Built distributed-tracing observability for a multi-agent system so every autonomous action emits a single, unified, auditable trace tree — the provability layer regulated environments require. Diagnosed and fixed fragmented trace roots via standards-based context propagation, and caught an automated audit gate emitting a false positive — overriding it with ground-truth evidence rather than trusting the green check.

AI Override Discipline Failure Mode Engineering
Read the postmortem →

Zero-Operator Content Pipeline — Outage Recovery

Hardened a multi-tenant, zero-operator content-automation pipeline across a 60-day build cycle. Root-caused and recovered a 10-day publishing outage whose underlying failure was a falsely-green verification signal — the system reported success while posts were silently failing — by instrumenting the publish path with distributed-tracing verification spans and an honest three-state status vocabulary (published / unverified / failed).

Evaluation-First Architecture

Production MCP Infrastructure

Designed and operates custom Model Context Protocol servers on managed serverless infrastructure: a unified Google Workspace server (Drive, Docs, Sheets, Gmail, Calendar) behind a single governed, least-privilege interface for an autonomous multi-agent system, backed by a substantial automated test suite; and a YouTube transcript-extraction server built to solve a production failure mode directly — relocating calls to a cloud-hosted origin to defeat IP-level rate-limiting and restore reliable extraction at volume.

Failure Mode Engineering Architectural Intentionality
PUBLISHED
2025

The Autonomous Operations Methodology

Systems Design · Whitepaper

A working thesis on where enterprise operations are heading — toward increasingly autonomous, lights-out execution — and the comprehension gap that decides which organizations get there and which stall.

Full taxonomy Deployment protocols
Read the thesis →