Building Self-Improving AI Agent Hierarchies with Paperclip Plugins

If you're running AI agent hierarchies, you've probably noticed the gap: agents complete tasks, but nothing checks if the output is actually good. There's no feedback loop, no auto-retry, and no way to catch performance degradation before it costs you. I built a set of 4 plugins for Paperclip AI that add a self-improvement layer to multi-agent setups (works with Paperclip-managed OpenClaw agent teams too). Here's how the architecture works. The Problem A typical agent hierarchy looks like this: CEO Agent [Opus] - decomposes goals, delegates CTO Agent [Opus] - makes tech decisions, delegates Worker Agent [Sonnet] - executes tasks Tasks flow down. Results flow up. But there's no quality layer. If the Worker produces bad output, the CEO doesn't know until a human checks manually. The Solution: An Event-Driven Feedback Loop Four plugins, each handling one part of the loop: task.created -> Skill Router assigns tools/agent type task.completed -> Performance Tracker logs outcome -> S