The Governance Architecture for AI Already Exists
- Raimund Laqua
- Apr 14
- 1 min read
AI is pushing humans out of the loop. The response many are taking is to figure out how to put humans back in. That is the wrong response.
The answer is not human-in-the-loop. The answer is agent-in-the-loop.
Train AI agents to participate in the governance loops that already exist. AI agents are replacing human workers who operated within those loops every day — workers who followed SOPs, escalated exceptions, maintained standards, and kept promises. When you remove those humans without training AI to participate in those same loops, you don't gain efficiency. You lose governance.
Most organizations are responding by drafting AI-specific policies, standing up AI ethics committees, and bolting guardrails onto agents from the outside. This will fail — because it treats AI governance as separate from organizational governance. The governance architectures needed to govern AI agents are not new. They already exist.
Requisite Organization provides the accountability structure. Promise Theory provides the commitment model. Cybernetic regulation provides the control architecture. Policy deployment provides the alignment mechanism. These are proven foundations that serious organizations already operate under.
In our new paper, "Promise Agents: Governing Humans and Machines Through Lines of Regulation," we introduce the Promise Agent — an agent, human or machine, that must formally declare its commitments, agree to keep them, and self-regulate to ensure that it does.
Self-regulation within the existing governance architecture is a condition of entry into the enterprise workforce. Not human-in-the-loop. Agent-in-the-loop.
The question is not whether these practices work. The question is whether the broader market will adopt them — or learn the hard way why they exist.
Link to the full paper below.
