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Sage AI
AI Ops Agent Workbench
AI Observability
Full-lifecycle AI observability
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Monitors
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Telecom operators have comprehensive infrastructure monitoring, but tools are siloed, creating severe data silos. Resource, network, and business data cannot be correlated, and front‑end user observability is lacking. Fault diagnosis requires cross‑checking multiple platforms. Operators are pushing for tool integration but lack standardized implementation paths and a full‑domain observability framework.
Root‑cause localization time reduced, outperforming the client's 24.7‑minute baseline – a 20%+ improvement.
Using RUM front‑end data collection, client‑side crash rates were reduced from over 10% to below 2%.
Integrates core client-side and page data into the platform; enables user session journey tracing, page performance probing, and collection of errors and user on-site data. Expected outcomes: effective reduction in user complaints and page crashes, improved engineering stability.
Leverages the platform's unified full-domain data foundation; standardizes the full-domain data model to break down barriers among metrics, logs, call chains, user experience, and infrastructure data. Eliminates data silos that constrain digital transformation progress; effectively compresses MTTR fault handling time; enables native linkage between monitoring collection and deep analysis; significantly reduces reliance on senior experts for troubleshooting.
IDC China Semiannual IT AI Operation Software Tracker, 2025H2
Cycle™ for Infrastructure Strategies in China, 2026
Market Guide for Intelligent IT Monitoring and Log Analysis Tools, China