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Graph Intelligence
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Graph-centric agentic intelligence
Graphs encode relationships and provide scaffolding for causal inference and compositional reasoning, which is essential for AI agents reasoning over complex networks. The post traces the evolution of graph intelligence from passive topology models to active agentic reasoning, culminating in a three-pillar approach for root cause analysis. The approach combines a continuously synchronized network digital twin graph with a cascaded pipeline of graph algorithms—decomposition, clustering, and centrality ranking—that narrow the search space. An agentic orchestration layer selects and composes these algorithms adaptively based on failure characteristics, performing complexity triage and consulting an incident knowledge base. The system achieved root cause analysis in minutes on commercial networks, demonstrated with NTT DOCOMO at MWC.
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