co-reference

The CO methodology is a domain-agnostic framework for orchestrating AI work under human oversight using clear context, guardrails, and process discipline. It replaces ad hoc prompting with a structured approach built on eight first principles and a five-layer architecture.

The CO architecture supports institutional knowledge by applying a five-layer model and a six-phase workflow. This structure enables context loading, vocabulary alignment, and methodology recall to maintain reliable human-AI collaboration.

You apply the CO workflow by following its six-phase model to structure human-AI collaboration. This process integrates institutional knowledge and oversight through context loading and vocabulary alignment without needing external dependencies.

CO serves as the domain-agnostic foundation for domain-specific applications like COC, CARE, and EATP. It provides the underlying principles and architecture these institutional knowledge systems use to orchestrate AI tasks under human oversight.

No, you do not need external dependencies to use the CO methodology reference. It functions as a concise conceptual guide that supports context loading, vocabulary alignment, and methodology recall entirely on its own.

You should use the CO methodology instead of ad hoc prompting when you need reliable AI collaboration with institutional knowledge and oversight. It provides process discipline through first principles, structured architecture, and defined workflow phases.