Purpose and scope of NORP

[garniergeorges]

NORP (NeuraScope Orchestration Reference Patterns) defines a set of architectural reference patterns for building AI orchestration engines that are production-ready.

This discussion is here to clarify what NORP is, what it intentionally covers, and what it does not try to standardize.

What NORP is

NORP focuses on the orchestration and execution layer of AI workflows, especially in SaaS B2B and multi-tenant environments.

The current specifications (NORP-001 to NORP-007) cover topics such as:

• Pre-execution structural validation
• DAG correctness and cycle detection
• Deterministic execution ordering
• Context and resource isolation
• Execution-scoped resource pooling
• Cost estimation and fail-fast behavior

NORP is not a framework or a library.
It does not prescribe implementations, languages, or runtimes.
It provides architectural guarantees and invariants that an orchestrator can choose to implement.

What NORP is not

NORP deliberately does not standardize:

• LLM protocols or APIs
• RAG, memory, or vector storage mechanisms
• Prompt formats or model-specific features
• Platform-level governance (RBAC, auth, billing, compliance workflows)

These concerns belong to other layers of the stack and may be addressed by separate specifications in the future.

Why NORP exists

Modern AI orchestrators execute non-deterministic components, allocate costly resources, and operate in multi-tenant environments.

Implicit assumptions in these systems quickly become production risks.

NORP exists to make those assumptions explicit, verifiable, and auditable.

Status

NORP is published early on purpose.
The specifications are extracted from real production experience and are expected to evolve through feedback, discussion, and independent implementations.

This discussion is open to:

• Clarification questions
• Scope challenges
• Architectural feedback
• Suggestions for evolution

If you are implementing an orchestrator or evaluating one, your perspective is especially valuable.