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Explore Nokia's AI-driven approach to root cause analysis designed to tackle complex day-two operational challenges in network environments. Learn how Nokia's deep RCA tool moves beyond traditional port-up/port-down monitoring by correlating end-to-end application connectivity from VM to VM across all network layers, including underlay, overlay, and control plane infrastructure. Discover the chronic problem of hidden impairments or gray failures where physical links appear active while protocols or services fail, and understand how AI can dramatically compress troubleshooting time. Watch a live demonstration on the EDA SaaS platform using real hardware Spine-Leaf network topology, where a gray failure is intentionally introduced by impairing a fiber link that keeps interface status "up" while causing BFD and BGP protocol failures. Examine Nokia's multi-agent workflow architecture that uses a planning model to determine which tool-calling agents to deploy, followed by agents that gather specific data from logs, topology, and configuration before filtering and passing information to a reasoning model. Understand how this agentic-based data curation approach reduces costs and prevents AI hallucinations in mission-critical network environments. See the system successfully identify challenging issues like MTU mismatches and explore the "Time Machine" feature that enables operators to select past timeframes and run AI-driven root cause analysis on historical data. Learn how the comprehensive AI-generated reports provide human-readable summaries, confidence scores, and specific evidence gathered by agents to solve complex logic puzzles that would typically require hours or days of manual engineering diagnosis.
