I Build AI Systems.
Then I Document What Breaks.
I'm Joe Cairns — an AI practitioner focused on building agents, automation workflows, and infrastructure that actually works in production. This site is where I share the real lessons: the architecture decisions, the tradeoffs, and the failures that don't make it into the polished demos.
From Curiosity to Production AI
I didn't start in AI. My background is in enterprise technology — systems integration, infrastructure, and the messy reality of making complex software work at scale. That foundation turned out to be exactly the right lens for AI.
When large language models became capable enough to build on top of, I went deep. Not into the theory — into the practice. What does it actually take to deploy an AI agent that handles real enterprise workflows? What breaks when you move from a demo to production? How do you build RAG systems that retrieve the right context, not just any context?
The answers weren't in blog posts or YouTube tutorials. They were in the building — in the failed deployments, the architecture rewrites, the observability gaps that only showed up under real load. So I started documenting everything.
Today I build AI agents, MCP servers, automation workflows, and RAG systems — primarily focused on enterprise operations, engineering productivity, customer experience, and infrastructure management. I open-source what I can, write about what I learn, and demonstrate working systems rather than just ideas.
How I Got Here
First contact with LLMs
GPT-3 dropped and I couldn't stop thinking about what it meant for enterprise software. Started experimenting with prompt engineering and API integrations.
Building the first agents
Moved from prompting to building — first autonomous agents using LangChain, tool use, and memory. Learned quickly that reliability is the hardest problem.
RAG systems & enterprise context
Deep focus on retrieval-augmented generation for internal knowledge bases. Built and rebuilt chunking strategies, embedding pipelines, and evaluation frameworks.
MCP servers & infrastructure AI
Adopted the Model Context Protocol early. Built MCP servers connecting agents to enterprise tools — incident management, infrastructure queries, runbook execution.
Production systems & open-source
Shipping production AI systems, contributing patterns and templates to open-source, and writing about what actually works in enterprise AI adoption.
Areas of Work
AI Agents & Automation
Autonomous agents that take real actions — not just generate text. Multi-step workflows, tool use, memory, and the reliability patterns that make them production-ready.
MCP Servers
Building Model Context Protocol servers that connect AI agents to enterprise tools without custom glue code. Infrastructure, incident management, and internal APIs.
RAG Systems
Retrieval-augmented generation for internal knowledge — engineering docs, runbooks, policies. Custom chunking, embedding strategies, and evaluation frameworks.
Enterprise AI Adoption
The practical side of bringing AI into organizations — integration patterns, compliance constraints, change management, and measuring what actually moves the needle.
Let's Connect
If you're building AI systems, thinking through enterprise AI strategy, or just want to compare notes on what's working — I'm always up for a conversation.