My Work: From Principled Research to Practical Implementation
Here is a selection of my major projects. Each is an exploration in building systems that are not just intelligent, but also transparent, auditable, and accountable by design. Click on any project to see a detailed case study of the research, architecture, and outcomes.
Ethical AI Systems
Architect
A governable, multi-agent AI system built on the principles of my research into transparency and user control.
Problem: Autonomous AI systems lack accountability, eroding user trust and agency.
Approach: Implemented the “Auditable Governance Schematic” from my research, featuring a “Policy-as-Code” gateway that vets every memory operation to ensure user-defined rules are never violated.
Outcome: A working prototype of an AI system that is accountable by design, serving as a reference architecture for building ethical, personalized AI.
Cognitive AI Analytics
NeuroTrace
A reproducible analytics framework that models the cognitive growth of AI assistants, turning conversation logs into research insights.
Problem: “Personalization” in AI is often a black box; we need structured methods to observe how an AI’s behavior actually evolves over time.
Approach: A deterministic CLI pipeline transforms raw conversation logs into analyzable interaction graphs, mapping messages to cognitive zones like Planning, Memory, and Feedback.
Outcome: Resulted in a formal research paper identifying key behavioral patterns, including a recurring
Feedback -> Planning corrective loop observed in ~26% of feedback events.Portfolio & AI Interface
MD Grid Engine
The component-driven content engine for this portfolio, designed to be the future UI for visualizing Architect's cognitive processes.
Problem: I needed a system to author component-rich content without a heavy CMS, and a visual layer to eventually render complex AI decision traces.
Approach: A custom Next.js engine with a runtime MDX compiler. A parser maps Markdown blocks to a registry of UI components like Grids and Cards.
Outcome: This portfolio website, which is fully powered by the engine and is ready to integrate with Architect as its primary user interface.
Agentic Workflow & State Management
Communication Agent (MVP1) – Triage & Response System
A fully engineered MVP that connects to Gmail, uses an external LLM (Architect) for triage and drafting, and manages ticket state (SQLAlchemy) via a comprehensive API and server-rendered UI.
Problem: Transforming raw, continuous email streams into auditable, stateful tickets requires a robust, production-grade agent system with secure authentication and state management.
Approach: Built a backend-first MVP using FastAPI and SQLAlchemy to manage ticket flow (NEW -> NEEDS_ACTION -> DONE), securely integrating Gmail via OAuth and abstracting LLM calls with a pluggable adapter.
Outcome: Delivered a production-ready, stateful system that automates the core support lifecycle, provides four real-time reporting boards (Jinja2), and adheres to strict security protocols for credential management.
Roadmap
Current Focus & Future Work
Now Shipping:
- The MD Grid Engine (this site’s content system)
- NeuroTrace core analysis pipeline (patterns, drift, trace reports)
In Progress:
- Architect: Refining the planner/router and building the v2 evaluation harness
- NeuroTrace: Packaging for public release and adding figure-generation helpers
Up Next:
- Integrating Architect with the MD Grid Engine for live, visual demos of AI decision-making.