Knowledge-Based Agentic AI for Multi-Agent AIOps

This document is a white paper that discusses the role of knowledge-based systems in enhancing multi-agent AIOps. It outlines the limitations of traditional AIOps, which often struggle with context gaps despite tracking KPIs. The paper emphasizes the need for operators to transition from network-centric operations to knowledge-driven approaches that prioritize data management and contextual understanding. It details how knowledge-based systems can improve decision accuracy, dynamic resilience, and shift operations from reactive to proactive models. The paper further explains the processes involved in implementing open knowledge-based systems, including data discovery, knowledge categorization, and shared intelligence among multi-vendor agents. Additionally, it presents use cases demonstrating how knowledge-driven automation can enhance network insights and operational efficiency, particularly in diagnosing and resolving performance issues in complex network environments. The paper serves as a comprehensive guide for understanding the foundational elements and advantages of knowledge-based systems in the context of AIOps.