Blog
A five-part series on why enterprise AI needs a harness system — an execution layer around agents, not another agent factory.
Why Open Cradle
Kubernetes became the runtime for containers. Linux became the runtime for applications. Enterprise AI needs a runtime of its own — not another framework or agent builder, but a harness system for intelligent systems. That is what we are building.
The neuro-symbolic runtime
Two modes of reasoning, interleaved at execution time: the model proposes, the symbolic layer checks, the ontology bounds what is possible, policy decides what is permitted. This is where explainability and reproducibility stop being aspirations.
What a harness system is
In engineering, a harness is a test rig: the controlled environment you run a system inside so you can observe and constrain its behaviour. Enterprise AI needs the same layer — and almost nobody is building it.
Why RAG will never be a corporate brain
Almost all enterprise AI today is documents plus a vector index plus an LLM. That is excellent for finding information and nearly useless for making decisions — because expertise is not stored in documents, it is stored in the structure between them.
Why the AI Factory is a dead end
Load documents, build a RAG index, plug in an LLM, get an assistant. Every AI Factory pitch looks like this — and most enterprise projects built on it end as demos. The problem is not the model, the GPUs, or the retrieval quality. It is the missing execution layer.