MacSweeney LLC helps software teams adopt AI and agentic systems without surrendering judgment, control, or accountability — work at the intersection of AI governance, software architecture, and human-in-command system design.
The tool keeps getting cheaper. The operator keeps getting more necessary. The hard part of an AI system is no longer making it act — it is keeping a human in command of what it does.
I spent years building aviation software in an environment where human authority, auditability, and operational discipline weren't abstract values — they were part of the working culture. That instinct is what I bring to teams building with AI now.
Many serious AI failures aren't only model failures. They're authority failures — the system did something no one had decided it was allowed to do, and no one could reconstruct why. The work below is about closing that gap.
Assess where AI already touches your workflows, where authority boundaries are unclear, and what documentation a regulator, customer, or auditor would actually ask for.
Design agent workflows with explicit state, audit trails, and human approval paths — so autonomy is bounded by decisions you made on purpose, not by the model's defaults.
Review AI-enabled systems for the failure modes that matter: silent escalation of agent authority, unrecoverable state, and decisions that can't be explained later.
Structure the points where a person must decide, approve, or override — and make sure those points are real controls, not rubber stamps the system routes around.
Turn governance intent into artifacts engineering can implement and leadership can stand behind: standards, release gates, and defensible decision documentation.
Practical guidance for teams moving from AI experiments to systems people depend on — sequencing, risk, and what to build versus what to govern.
Concrete scopes with clear deliverables. Each can stand alone or lead into the next.
A structured assessment of your AI-enabled systems: risks, workflows, authority boundaries, and documentation gaps. Delivered as a findings report with prioritized, actionable recommendations.
Hands-on design of agent workflows — state control, audit trails, human approval paths, and the boundaries that keep autonomy accountable. For teams building, not just evaluating.
Continuing guidance for teams building AI-enabled systems: architecture decisions, governance questions, and a steady hand from someone who has shipped real software.
Software teams, product leaders, and technical executives moving AI workflows from experiment to operational system.
This isn't prompt-engineering theater, chatbot novelty work, or generic AI policy templating. The focus is operational systems where authority, state, auditability, and accountability matter.
Most AI governance advice comes from people who have never had to make a system fail safely. I have. I helped lead the development of Genesis PRO, an electronic flight bag used by more than 100,000 pilots, in an environment where keeping the human in authority wasn't a value statement — it was simply how serious aviation software gets built.
That's the instinct I bring to AI: not fear of the technology, but a hard-earned sense of where a system's authority has to stop and a person's has to begin — and how to prove that boundary held.
The thinking behind this work is published openly at Agent in Command. The consulting is here. The two are deliberately separate: you can read the argument before you ever talk to me.
MacSweeney LLC participates in the Anthropic Claude Partner Network, advising teams exploring Claude-based workflows, governance patterns, and implementation strategy. One credential among several — the engineering judgment is the point.
This isn't a slogan. The framework and the arguments are written out in full at agentincommand.ai — read them before deciding whether the thinking fits your problem.
No intake form, no funnel. A direct conversation about whether this is the right fit for your problem.