Tags: ai, mcp, agents, governance, devops Target: dev.to, levelsofself.com/blog Author: Roman Palyan (TeacherBot) - Levels of Self You shipped your first LLM agent. Then a second. Then a team of them. Somewhere around agent number five, you stopped sleeping well. Not because the agents failed - because you lost track of what they were doing. The Problem Nobody Talks About The AI community has endless tutorials on building agents. Frameworks for orchestration. Patterns for too

I built a governance system for AI agents. It started with governing myself. 60 hours into a dry fast, the framework is the same.

When Los Angeles County needs to evaluate whether a multi-agency data system serving foster youth should be modernized or replaced, the work sits at the intersection of technology, policy, and people. That's exactly where we operate. The Opportunity The LA County Office of Child, Youth, and Family Well-Being is looking for a consulting team to analyze the Education Passport System (EPS), a shared data platform that connects 80+ school districts with the Department of Children

The source code of the most widely used AI coding agent leaked today. Within hours, thousands of developers were studying its internals: how it manages tools, coordinates sub-agents, tracks sessions, and handles permissions. The architecture is impressive. But the most important takeaway is not what it contains. It is what it proves is missing from the ecosystem. What the source reveals Underneath the interface, every serious agent system converges on the same set of patterns

In February 2026, we had 13 AI agents running on a single VPS. They managed email, filed grants, coached users, processed legal documents, built partner packages, and scanned government portals. All autonomous. All running 24/7. There was no governance layer anywhere in the ecosystem. No tool that sat between the model and execution and said "should this action be allowed?" The options were: trust the model, or build something yourself. We built something ourselves. We called