AI/ML-based network operation is fundamentally about creating a cognitive, self-driving network. This requires a framework where machines can sense, analyze, and act without human intervention. This is achieved through:

1. Closed-loop automation (D): The operational paradigm where the system continuously monitors the network, uses AI/ML to analyze data and make decisions, and automatically executes corrective or optimizing actions.

2. Open APIs (B): These are essential for the AI/ML system to collect telemetry data from diverse, multi-vendor network elements and to programmatically enforce the decisions made by the AI engine.

3. Pre-defined policies (A): These provide the business and operational intent, acting as "guardrails" that guide the AI/ML algorithms toward achieving specific goals, such as maintaining a certain quality of service or minimizing energy consumption.

C. Standard Protocols: While Open APIs often utilize standard protocols (e.g., NETCONF, REST), the API itself is the more direct and critical requirement for enabling the programmability and data exchange needed for AI/ML operations.

1. Nokia Bell Labs End-to-End 5G Networking, 2nd Edition. Chapter 10, Section 10.4, "Automation." The text states, "This [zero-touch automation] is achieved by using closed control loops that are driven by AI/ML-based analytics and guided by high-level policies that express the operator’s intent." This statement directly supports the necessity of closed-loop automation (D), AI/ML, and pre-defined policies (A). The chapter also emphasizes the need for open interfaces for programmability (B).

2. ETSI GS ZSM 002 V1.1.1 (2019-08), "Zero-touch network and Service Management (ZSM); Reference Architecture." Section 5.2, "High-level architectural principles," describes the core tenets of a ZSM-enabled architecture, which include: "The ZSM framework enables closed-loop automation..." and "The management services are designed to be policy-driven and intent-based." Section 5.3, "Architectural characteristics," notes the importance of "Open and standard interfaces" to expose management capabilities via APIs. Nokia is a key contributor to this standard.

3. Nokia White Paper, "AI in telecommunication networks: a path to cognitive automation." This document outlines the evolution toward cognitive networks, emphasizing that AI/ML engines require three key components: vast amounts of data (accessed via open APIs), powerful algorithms, and the ability to take action through closed-loop automation, all while being steered by business objectives (policies).