AIPCH03 — Declarative Specification

“Defined via Structured Declarative AI Specification”

What AIPCH03 is really asserting​

AIPCH03 is not asserting that:

“The AI Product is defined using YAML, JSON, or configuration files.”

It is asserting that:

The AI Product is represented through a structured, machine-readable specification that fully captures its intent, behavior, constraints, and interfaces — such that the platform can deterministically interpret, validate, and compile it into execution.

Declarative here means:

The product is represented as a complete, structured description — not as procedural implementation.

The Essence (HDIP + AIPS Interpretation)​

An AI Product is declaratively specified if and only if:

1. The product is represented through structured artifacts (e.g., AIPROD, AIPDS)
2. The specification is complete enough to drive compilation
3. The specification is interpretable by machines, not dependent on human interpretation

If the product definition exists as:

• code
• scripts
• notebooks
• undocumented pipelines

then AIPCH03 is not met, even if the system is functional.

Important Distinction​

AIPCH03 must be clearly separated from:

🔹 AIPCH04 — Declarative Intelligence Composition​

👉 AIPCH03 ensures:

The product exists as a structured, portable, computable artifact

Positive Criteria — When AIPCH03 is met​

AIPCH03 is met when all of the following are true:

1. The AI Product has a structured, canonical specification​

The product is defined through artifacts such as:

• AIPROD (semantic/product definition)
• AIPDS (deployment specification)
• associated policy, trust, and governance artifacts

These are:

• machine-readable
• version-controlled
• uniquely identifiable

2. The specification captures the full product definition​

The specification includes:

• purpose and intent
• behavior and expected outcomes
• inputs and outputs
• constraints (risk, policy, safety)
• dependencies (AI/Data Products where applicable)
• access and interface definitions

It is not partial or implicit.

3. The specification is the source of truth​

The AI Product:

• is derived from the specification
• can be regenerated from it
• is not defined elsewhere (e.g., hidden in code)

There is no divergence between:

• declared product
• deployed product

4. The specification is machine-interpretable​

The platform can:

• validate the specification
• compile it into execution artifacts
• enforce governance from it

No human interpretation layer is required.

Negative Criteria — When AIPCH03 is not met​

AIPCH03 is not met if any of the following are true:

❌ Product definition exists primarily in code or pipelines​

Examples:

• behavior defined in notebooks or scripts
• orchestration logic defines product behavior
• model code implicitly defines product

This is procedural definition, not declarative specification.

❌ Specification is incomplete or symbolic​

Examples:

• partial metadata without behavior definition
• documentation separate from execution
• missing constraints or policy definitions

This creates ambiguity and breaks compilation.

❌ Specification is human-readable but not machine-actionable​

Examples:

• Word documents
• Confluence pages
• informal descriptions

These are descriptive artifacts, not specifications.

❌ Specification requires manual interpretation​

Examples:

• engineers must “read and implement” the spec
• deployment depends on human translation
• ambiguity exists in how behavior should be realized

This violates determinism.

Edge Cases (Important Guidance for Agents)​

Case 1: “YAML/JSON config defining pipelines or models”​

❌ Not met

Rationale:

• syntax is declarative
• semantics are still procedural
• describes how, not what

Case 2: “Specification exists but incomplete”​

⚠️ Partial

Rationale:

• structure exists
• but insufficient for full compilation
• acceptable in early stages only

Case 3: “Full AIPROD + AIPDS with compilation capability”​

✅ Met

Rationale:

• product fully defined as structured artifacts
• platform can compile and deploy deterministically

Case 4: “Specification auto-generated from code”​

⚠️ Partial

Rationale:

• useful for transition
• but source of truth is still procedural
• true state requires reverse (code generated from spec)

Evidence Signals an Agent Should Look For​

Authoritative evidence:​

• AIPROD artifact exists and is version-controlled
• AIPDS generated and linked to product
• specification uniquely identifies product instance/version

Supporting evidence:​

• platform validation of spec
• regeneration capability (spec → runtime)
• consistency between declared and deployed state

Red flags:​

• reliance on notebooks or scripts
• divergence between documentation and execution
• undocumented behavior or constraints

How an Agent Should Decide​

Decision rule (simplified):

If the AI Product cannot be fully understood, validated, and reproduced from a structured, machine-interpretable specification, AIPCH03 is not met.

Why AIPCH03 Is Non-Negotiable​

Without AIPCH03:

• products cannot be reliably governed
• behavior cannot be audited
• deployment cannot be deterministic
• composition becomes fragile
• automation breaks down

AIPCH03 enables:

• platform compilation
• governance enforcement
• reproducibility
• interoperability

Canonical Statement (for AIPS)​

AIPCH03 is satisfied only when an AI Product is fully represented through structured, machine-interpretable declarative artifacts that capture its intent, behavior, constraints, and interfaces, enabling deterministic validation, compilation, and deployment by the platform.