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Cisco is addressing the pervasive fragmentation in IT operations, where critical problems often take days or even months to resolve due to disparate data sources and manual correlation across various domains like applications, security, compute, and networking. With different products and specialized teams managing these silos, efficient troubleshooting is a significant challenge. Cisco’s response is Agentic AI, a strategy that leverages its extensive portfolio of world-class solutions across these domains to create a unified operational experience. This initiative introduces Cisco Cloud Control, an AI-native management platform designed to bring together data, context, and IT personas from across the infrastructure.
At the core of Cisco Cloud Control are two main AI components: AI Canvas and AI Assistant. The AI Assistant functions as a chatbot, enabling users to ask contextual questions about any element connected to Cloud Control. AI Canvas serves as a dynamic, collaborative environment where cross-product data and diverse IT personas converge to triage issues. It features a central canvas board where a purpose-built AI model automatically renders relevant widgets and data in real time, a concept referred to as Generative UI. This collaborative space allows different IT roles, such as application, compute, or network administrators, to work together with shared visibility of the troubleshooting context, while still enforcing granular role-based access control for actions.
The underlying multi-agent architecture enables parallelized debugging, where agents associated with products like Nexus Dashboard, Nexus Hyperfabric, and InterSight simultaneously investigate their respective domains for problem symptoms. This approach dramatically accelerates root cause analysis, moving away from sequential troubleshooting. A demonstration illustrated how a slow AI training job was quickly diagnosed, correlating high GPU utilization with network degradation traced to CRC errors and an overheated optical module. The platform facilitated seamless collaboration between compute and network administrators, proposed a remediation, and even generated a comprehensive report detailing the problem, investigation timeline, remediation steps, and future recommendations, streamlining communication and post-incident analysis for a more efficient and assisted operations model.
Personnel: Arun Annavarapu
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