About
I’m Greg Wilson, an experienced CTO and CIO who still likes being close to the code. I write about production AI and the engineering work around it: agents, infrastructure, DevOps, internal tools, and the operating details that make software reliable enough for real work. That bias toward useful software started early.
My first useful program was job bidding and estimation software I built with my dad in QBasic as a young man. He used it in his sheet metal shop. That made software real to me before it felt abstract. It was not a toy project or a syntax exercise; it was a tool someone used to run a business. Later, I rebuilt versions of the same tool while learning other languages because I understood the problem well enough to focus on the craft. That set the pattern: build tools that change how real work gets done.
That pattern has held for more than 20 years. I’ve built and run production software across product engineering, identity, integrations, regulated workflows, cloud infrastructure, DevOps, security, compliance, data, and delivery. The common thread is practical: build the thing, understand the workflow, and stay responsible for how it behaves in production.
I’m now the Chief Information and Technology Officer at Buoy Software, where I started as a founding engineer. I helped build the first product, then helped turn it into a set of services for regulated healthcare workflows used by blood and plasma centers. The work includes FDA 510(k)-cleared medical devices, so I think a lot about where new technology can remove friction, automate the right work, and still leave people confident in what happened.
My AI work starts from that same idea: remove friction without losing confidence in the system. I build and operate agents that can help with real work, including Slack-first assistants, coding-agent orchestration, Claude Code skills and plugins, Model Context Protocol (MCP) integrations, browser and research automation, content workflows, and internal AI enablement for engineering teams.
That is why I write about agents the way I do. Once an agent can write data, call tools, notify people, or change a workflow, it stops being a demo. It becomes part of the system people rely on to get work done.
When software becomes part of real work, evidence matters. You need to know what changed, who approved it, what the logs say, how rollback works, and who owns the failure. Regulated software taught me that lesson, and AI agents need the same discipline.
What I write about #
AI agents are production systems when they become part of real work. I’m interested in the practical version of that problem: how teams use AI to remove friction, automate the right work, and still understand what happened.
That leads to the operating details I write about:
- agent observability and evals
- tool boundaries and blast radius
- deployment and approval gates
- secrets, cost controls, and rollback
- agent-assisted software delivery
- governance inside the workflow
- agent readiness and operating reviews
- the platform work that lets teams use AI without guessing
I want each post to give you a sharper question, a pattern to try, or a failure mode to watch for in your own systems.
Practical AI #
I co-contribute to Practical AI , Damian Galarza’s newsletter for builders working with AI.
It covers the patterns, tradeoffs, and lessons that show up when AI moves from prototype to real work.
Contact #
Email me at hello@imawilson.com .