I'm Colin McNamara, Field CTO at AHEAD. My thesis is simple: we are rewriting the business operating system with AI. I help technology and SaaS clients put agentic AI to work inside the software development lifecycle and run it safely in production, with the governance and evaluation discipline (LLM-as-judge, continuous auditing) that makes it defensible in regulated industries like financial services, healthcare, and energy.
Before AHEAD I spent 25 years at the boundary where research becomes production: Network Architect on Apple's iCloud build, four global services at Oracle Cloud, and a builder on two companies that were acquired. I also founded AI ventures of my own, including Self-Improving Code and a legal-tech AI venture, where I used AI to rewrite the business operating system end to end. And I founded Austin LangChain AIMUG, now a 2,100+ member community expanding internationally.
My Notes Philosophy
This site is essentially my public notebook. These are my working notes: processes I find useful, projects I'm exploring, and thoughts worth preserving. They're written primarily for myself as reference material, but I share them publicly in case others find them helpful.
You'll find three types of notes here:
- Process Notes - How-tos and procedures I've developed or discovered
- Project Notes - What I'm building, learning, and experimenting with
- Thought Notes - Reflections, insights, and perspectives on technology and business
I believe in learning in the open and sharing knowledge as it develops, not just when it's polished. If something here helps you solve a problem or sparks an idea, that's a bonus.
What I Do Today
- Field CTO at AHEAD: agentic AI in the software development lifecycle, AI governance, and defensible AI for regulated and high-consequence environments (technology, SaaS, hyperscale)
- Founder of Austin LangChain AIMUG: a 2,100+ member community of AI practitioners, now expanding internationally (Panama)
- Builder of AI ventures: Self-Improving Code and a legal-tech AI venture applying agentic AI to high-consequence, regulated workflows
The AI Transformation That Started It All
My journey with modern AI began when I discovered the "Chat with data challenge" by Harrison Chase, Founder of LangChain AI. It was a basic RAG workflow using pre-0.1 LangChain. I applied it to our manufacturer due diligence process at Always Cool Brands and uncovered fraud that would have led to an SEC violation. This single AI implementation saved our company and secured our supply chain.
That moment showed me the transformative power of modern Transformers and RAG architectures. What started as solving one problem evolved into automating every manual process slowing our teams down, from nutrition calculations to supply chain verification.
From Food Manufacturing to Production MLOps
Over two and a half years building Always Cool Brands, I learned that creating better-for-you products requires mastering every detail: reverse engineering formulas, navigating quality control, scaling production. Just like in my hyperscale infrastructure days, I automated the friction points.
Simple scripts became synchronous agents. Those evolved into asynchronous workflows using LangGraph. But the real breakthrough came when I implemented OpenTelemetry for complete observability, because observability is the key to moving from AI scripts to production MLOps infrastructure. Every decision, every action, every outcome became traceable and auditable.
The production patterns from Always Cool AI (a skills abstraction layer for portable agent orchestration built on MCP, Claude Agent SDK, and DeepAgents, with A2A Protocol for agent-to-agent communication and OpenTelemetry for enterprise-grade observability) now form the reference architecture behind Self-Improving Code. These patterns ensure control and compliance in industries where mistakes aren't an option.
From Hyperscale Infrastructure to Production MLOps
My technology foundation began on midnight shifts automating production for CIA surveillance systems in 1998. I moved to Silicon Valley at 19 with Stanford recruitment letters and near-perfect SATs, while serving as a Marine Corps reservist. That unique blend of Silicon Valley innovation and military discipline shaped my approach to solving complex problems.
Over 25 years, I've architected some of the world's largest web systems, including being brought in as the Network Architect for iCloud. I built a $180M/year business line from scratch, earned CCIE Storage certification (#18233), contributed to OpenStack and OpenDaylight, and reduced outages by 58% at Oracle. I've developed network software for Cisco Systems and created intellectual property that enabled major acquisitions. This deep infrastructure and business-building experience now powers my ability to build production AI & MLOps systems that scale. For the full journey, see my resume.
Community & Vision
Through Austin LangChain AIMUG, I lead a group of engineers "Learning in the Open," accelerating AI skills in our group, our companies, and in the communities we are members of. We believe the best way to advance AI is by sharing our discoveries, failures, and breakthroughs publicly. Our hackathons, collaborative projects, and regular meetups create a space where 2,100+ practitioners push the boundaries of what's possible together.
I believe AI will fundamentally transform how we approach supply chain transparency, business operations, and community problem-solving. My commitment is to open knowledge sharing and building intelligent, sustainable technological solutions that benefit everyone. I'm particularly passionate about using AI to eliminate harmful ingredients from our food supply while making supply chains transparent and efficient.
Where the experience comes from
The patterns I bring to AI did not start with AI. They came from running infrastructure where failure was not an option:
- Governance at scale: survived 38 enterprise audits and built systems for FDA and NRC requirements
- Reliability under pressure: kept the White House and Marine Corps online with 12 engineers
- Operating at scale: managed 116,000 devices and $70M budgets, and cut outages 58% at Oracle
- Observability first: pioneered hyperscale observability at Oracle, the discipline I now apply to agentic AI