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@@ -19,4 +19,97 @@ Focus on the developer inner loop, everything an AI engineer does on a laptop/de
 * An technical POC showing <10 min “idea-to-inference” path for cloud-native agent development on a developer laptop.
 * Clearly documented standards for OCI artefact standardization across runtimes and registries.
 * Specification / procedure to achieve MOF Class III compliant model distributions via any OCI registry.
-* Standardised process for leveraging model signing with artefacts-level provenance to support a verified end-to-end CI/CD reference pipeline including outer loop for AI engineering.
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+* Standardised process for leveraging model signing with artefacts-level provenance to support a verified end-to-end CI/CD reference pipeline including outer loop for AI engineering.
+
+## Project Scope & Intent - Cloud-Native AI Developer Workflow Interoperability
+
+### Overview and Intent
+AI developers today frequently work in fragmented local environments that are disconnected from cloud-native operational workflows. While emerging standards like ModelPack and OCI-aligned AI artifact initiatives provide the “packaging” foundations, there is no unified interoperability specification that defines how these artifacts must be structured, secured, and described to move seamlessly from a developers laptop into a Kubernetes-based production system.
+
+The goal of this initiative is to define a minimal Interoperability Specification (a “Compliance Profile”) for AI Artifacts. Rather than rebuilding the OCI layer structure, this initiative defines the **Standardized Metadata Contract** that must exist on top of packaging formats like **ModelPack**. This ensures that any <a href="https://github.com/cncf/foundation/blob/main/style-guide.md#1-cloud-native-and-open-source" target="_blank">“Cloud-Native Ready”</a> AI artifact contains the mandatory identity, security, and runtime information required that enables a cohesive developer inner loop and GitOps-driven delivery.
+
+This initiative intentionally builds on existing OCI-aligned packaging efforts rather than redefining artifact storage or layer mechanics.
+
+### Scope Overview
+This initiative defines the **Interoperability Layer** for AI artifacts, bridging the gap between raw packaging and operational deployment. 
+
+Within this scope, the initiative will explore and document:
+* **An Interoperability Profile Spec:** A set of mandatory annotation conventions and metadata requirements (the “Manifest Contract”).
+* **Compliance & Trust Requirements:** Standards for signing, SBOMs, and openness classification.
+* **Workflow Reference Patterns:** Validating the spec through “Laptop-to-Cluster” GitOps and runtime integration.
+
+The initiative is intended to encourage ecosystem alignment and workflow interoperability rather than define new standalone packaging specifications or runtime standards.
+
+### In-Scope Areas
+#### 1. The Interoperability Specification
+Define a structured, minimal specification for AI artifacts to be considered “Cloud-Native Interoperable”. This does not define OCI layering but specifies the mandatory metadata:
+* **Annotation Conventions:** Standardize keys for runtime frameworks (e.g., vllm), hardware accelerators (e.g., nvidia-gpu), and lifecycle status.
+* **Agentic Assets:** Standardizing the packaging of “skills”, prompt templates and workflow definitions. 
+  * To ensure interoperability, the internal format for skills will align with the <a href="https://agentskills.io/home" target="_blank">agentskills.io</a> community standard. 
+  * The spec defines how these standardized skills are encapsulated into the OCI layers for consistent distribution and discovery.
+  * The initiative may leverage Skill DLC as the primary reference for demonstrating how these assets are dynamically loaded and managed.
+* Interface Definitions: Define the "Ingredient List” for the different classes of artifacts (Models, RAG contexts, and Agentic Assets).
+
+This includes defining how artifacts relate to and compose with one another.
+
+#### 2. Metadata, Relationships, & MOF Mapping
+Define how artifacts describe themselves and their dependencies to enable cross-tool discovery:
+* **MOF-to-OCI Mapping:** Standardize how the LF AI & Data Model Openness Framework (MOF) classifications (e.g., Class I, II, III) are represented as machine-readable OCI metadata.
+* **Lineage & Authorship:** Standardizing metadata for provenance, versioning, and authorship to ensure clear ownership as artifacts move from local environments to registries.
+* **Relationship Mapping:** Defining minimal relationships to metadata conventions between related artifacts (e.g., model → skill → pipeline) within the OCI manifest.
+* **Dependencies & Compatibility:** A schema for declaring software dependencies and infrastructure requirements (e.g., specific CUDA versions or vRAM minimums) to ensure cross-runtime interoperability.
+* **Large Binary Asset Optimization:** Establishing best practices and metadata conventions for the efficient handling of large-scale binary artifacts (multi-GB model weights) within OCI registries, focusing on registry compatibility and layer deduplication. 
+* **Alignment with CNCF:** Build on existing efforts (e.g., <a href="https://modelpack.org/" target="_blank">ModelPack</a>, <a href="https://kitops.org/docs/modelkit/intro/" target="_blank">ModelKit</a>, <a href="https://github.com/redhat-ai-services/modelcar-catalog" target="_blank">ModelCar</a>)
+
+#### 3. Supply Chain Security and Transparency
+Define the mandatory “Trust Profile” for AI artifacts to ensure they are verifiable before entering production:
+* **Cryptographic Identity:** Standardize artifact signing and verification using Sigstore and Notary v2 at the point of creation on a developer's machine.
+* **Transparency Manifests:** Mandatory requirements for SBOM (Software Bill of Materials) generation and attachment for all artifact layers.
+* **Provenance Metadata:** Defining the "Hardened Provenance" requirements to ensure the journey from local experimentation to a secure registry is immutable and documented.
+
+The goal is to ensure artifacts are trusted and verifiable before entering CI/CD pipelines.
+
+#### 4. Developer Inner-Loop & Workflow Interoperability
+Define the operational patterns that allow the specification to be utilized in a portable "laptop-to-cluster" journey.
+* **Workflow Consistency:** Documenting how existing OCI-aligned tools (ModelPack, ModelKit, ModelCar) can produce artifacts that adhere to this initiative's compliance spec.
+* **Local Execution Patterns:** Reference patterns for running specified artifacts in local, container-based environments to ensure high-fidelity parity with remote clusters.
+* **Rapid Iteration Flow:** Validation of the spec through a reference implementation targeting a sub-10-minute "idea-to-inference" experience.
+
+#### 5. GitOps and Kubernetes Integration Patterns
+Define the "Handoff" patterns for how artifacts transition into production cloud-native systems.
+* **GitOps Delivery Patterns:** Reference architectures for pulling compliant artifacts into Flux or Argo CD workflows.
+* **Runtime Integration:** Standardized patterns for the seamless deployment of artifacts into serving platforms (e.g., <a href="https://kserve.github.io/website/" target="_blank">KServe</a>, <a href="https://github.com/vllm-project/vllm" target="_blank">vLLM</a>) and registration into model registries (e.g., Kubeflow Model Registry).
+* **Enterprise Requirements:** Ensuring the promotion spec accounts for air-gapped, regulated, and hybrid-cloud infrastructure constraints.
+
+#### 6. Real-World Deployment Considerations
+Ensure the approach accounts for:
+* Air-gapped and regulated environments
+* Enterprise security and compliance requirements
+* Regulated environments
+* Hybrid and multi-platform infrastructure
+* Resource-constrained developer environments
+
+This ensures the solution is practical and broadly applicable.
+
+#### 7. Ecosystem Collaboration
+This initiative will be developed in collaboration with:
+* ModelPack and related OCI-aligned initiatives
+* CNCF projects
+* LF AI & Data communities
+* OpenSSF and supply chain security initiatives
+* Kubernetes AI and platform engineering communities
+
+The intent is to align efforts across communities rather than define a solution in isolation.
+
+### Out of Scope Areas
+* Define new low-level binary compression or OCI layering techniques (deferring to OCI/ModelPack)
+* Define model architectures or ML training frameworks
+* Mandate specific vendor-locked developer tools
+* Standardize outer-loop pipelines beyond reference integration patterns
+
+### Definition of Success
+* **A Published Interoperability Spec:** A validated specification that existing tools can adopt to ensure cloud-native readiness.
+* **Cross-Tool Portability:** Demonstrated ability for an artifact built by one tool to be verified and deployed by a different runtime.
+* **The "10-Minute Flow":** A successful reference implementation demonstrating the journey from a local idea to a running inference service on Kubernetes.
+* **Ecosystem Alignment:** Broad adoption of the "Compliance Profile" metadata across CNCF and LF AI & Data communities.
+