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Integration & Composability

AI Products must be designed for integration into enterprise workflows and composability with other products.
This ensures they function not as isolated assets, but as building blocks in larger ecosystems.

Why This Matters​

  • Ecosystem Value → AI Products deliver greater impact when combined with other products.
  • Reusability → Composable design avoids duplication and siloed solutions.
  • Governance → Integration requires consistent enforcement of policies across interconnected products.
  • Innovation → Enables new emergent capabilities through composition (e.g., chaining AI agents).

Integration Requirements​

An AI Product must declare:

  • Supported interfaces & protocols → APIs, SDKs, messaging systems (Kafka, MQTT), event streams.
  • Interoperability standards → ONNX, MLflow, OpenTelemetry, OpenAPI, JSON-LD.
  • Dependency declarations → required upstream products (e.g., a Data Product).
  • Integration patterns → synchronous API call, async queue, publish/subscribe, agent orchestration.

Composability Requirements​

AI Products must support composability by exposing:

  • Clear input/output schemas → enabling chaining across multiple products.
  • Standardized metadata → for discovery and orchestration.
  • Composable workflows → declarative DAGs (Directed Acyclic Graphs), pipelines, or agent task graphs.
  • Reusability contracts → versioned interfaces that remain stable across upgrades.

Governance & Risk​

  • Composed products must inherit governance policies from all components.
  • Risks of composition (e.g., emergent bias, privacy leakage) must be declared.
  • Prohibited uses (see Prohibited Uses) apply recursively in composition.

Example​

AI Agent Orchestration Product

  • Integration:
    • Consumes outputs from multiple Data Products (financial transactions, customer profiles).
    • Publishes results to workflow automation tools via REST + Kafka.
  • Composability:
    • Chained with an LLM for natural language reasoning.
    • Uses declarative DAG for orchestrating multi-step inference.
  • Governance:
    • All composed products checked for compliance before deployment.

Summary​

  • AI Products must declare integration interfaces and interoperability standards.
  • Composability enables chaining, orchestration, and reuse across workflows.
  • Governance and compliance must extend to compositions, not just individual products.

Principle: An AI Product that cannot integrate or compose is an isolated asset — not a first-class product.