Engineering Intelligent AI Systems
Building multi-model AI platforms that orchestrate ChatGPT, Claude, Gemini, Grok, local LLMs, agent swarms, MCP servers, RAG pipelines, and enterprise automation.
AI Engineering Lab
Engineering the Future of Multi-Model AI
I'm Joe, and this is my AI Engineering Lab.
With decades of experience designing enterprise infrastructure, cloud platforms, and mission-critical systems, I'm now engineering the next generation of intelligent software.
My work focuses on building multi-model AI platforms that combine frontier AI models—including ChatGPT, Claude, Gemini, Grok, and local LLMs—into collaborative systems capable of solving complex real-world problems.
Inside the lab, I design and build:
- AI agents and multi-agent swarm systems
- Model Context Protocol (MCP) servers
- Retrieval-Augmented Generation (RAG) platforms
- Intelligent automation and orchestration workflows
- Enterprise AI architecture
- AI infrastructure and developer tooling
This isn't a portfolio of finished products. It's an open engineering lab where I document the architecture, experiments, lessons learned, failures, and engineering decisions behind building practical, scalable AI systems. My goal is to explore what works, understand why it works, and share those insights with others building the future of AI.
What I'm Building
Working AI systems — not just ideas. From autonomous agents to RAG pipelines, here's what I've shipped and what I've learned building it.
Enterprise Ops Agent with MCP
An autonomous agent built on the Model Context Protocol that handles infrastructure queries, incident triage, and runbook execution. Connects to internal tools via MCP servers — no custom glue code required.
RAG Pipeline for Engineering Docs
A retrieval-augmented generation system that indexes internal engineering documentation and answers developer questions with cited sources. Built with LangChain, pgvector, and a custom chunking strategy.
Why Most AI Agents Fail in Production
A deep-dive into the architectural tradeoffs that separate demos from deployed systems — covering memory, tool reliability, observability, and the human-in-the-loop patterns that actually work.
Engineering the Future of Intelligent Systems
Exploring how AI agents, multi-agent swarms, frontier models, enterprise systems, and automation work together to solve real-world problems.
Follow the Journey
New agents, workflows, and hard-won lessons — documented as I build them. No hype, just working systems and honest writeups.