IBM Launches Agentic AI for Smarter Networks

IBM has launched IBM Network Intelligence, a network-native AI solution designed to tackle the growing complexity of modern telecom and enterprise networks. Developed with IBM Research, the platform combines advanced time-series models with LLM-powered reasoning agents, enabling organizations to transform network operations and build trustworthy AI.

By automating issue detection and resolution, it helps network teams reduce reactive work, optimize resources, and unlock scalable automation across complex networks.

The main challenge is that network data is vast, relational, and scattered across domains, vendors, and formats, creating silos that hide critical insights. Current tools struggle to analyze these connections or understand real-time behaviors, leaving humans to manually piece together siloed data.

This labor-intensive process is slow, error-prone, and diverts resources from higher-value tasks. Despite improvements in automation and traditional ML, operators still can’t keep up with the demands of real-time service, low-latency performance, and zero downtime.

IBM Network Intelligence leverages Granite Time Series Foundation Models, compact AI models purpose-built for networking and developed by IBM Research. Pre-trained on extensive telemetry, alarms, and flow data, these models provide deep contextual understanding of network behavior. Unlike rule-based tools, generic ML, or LLMs, they detect hidden issues, offer early-warning signals without thresholds, and improve signal-to-noise accuracy.

The solution creates a unified pipeline for all network data, including design, vendor specifics, operations, and organizational rules.