Building a Production-Grade AI Agent Library: YAML Schemas, Composable Pipelines, and 6-Runtime Support

Most AI agent libraries are just folders of markdown files. You get prompts. Maybe nice formatting. But no way to know programmatically: What tools the agent actually requires to run What context it needs as input What format its output will be in Which agent should run next in a pipeline I spent the last few weeks building operator-agents to fix this. Here's what I learned. The Core Problem: Agents as Unstructured Prompts Take any popular agent library. Clone it. Look at a file: # Senior Developer Agent You are a senior developer. Review code carefully. Consider security, performance, and maintainability... This works for a human reading it. But for a pipeline orchestrator or an automation tool? It's opaque. You can't programmatically answer "what does this agent need to run?" or "what should happen after it finishes?" The Solution: AGENT_SPEC.md I designed a YAML frontmatter schema that every agent in the library must implement: --- name : senior-developer display_name : Senior Devel