Cisco Enhances AgenticOps to Address Complexity in Autonomous Networks
Cisco has updated its AgenticOps, an AI-driven operational model aimed at telecom companies transitioning to autonomous networks. The enhancements focus on networking, security, and observability, allowing IT teams to manage complexity effectively. AgenticOps utilizes advanced AI and unified network data to automate operations across various environments. The solution addresses industry demands for improved operational efficiency, enabling faster problem resolution, especially for service providers dealing with multi-vendor networks. The push for autonomous networks is driven by increasing operational pressures and the complexities introduced by 5G.
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Cisco has renewed its AgenticOps, an AI-based operational model for telecom companies transitioning to autonomous networks. This updated model focuses on networking, security, and observability, enabling IT teams to manage operational complexity efficiently.
AgenticOps leverages advanced AI and unified network data, including Agentic Workflows and AI Canvas, to automate tasks and reduce mean time to repair (MTTR). The solution supports IT operations in cloud, on-premise, and industrial environments.
Cisco emphasizes that autonomous networks can reduce operational costs and enhance service quality, yet many operators struggle with automation maturity. Agentic AI utilizes intelligent agents for self-configuration, optimization, and repair, adapting to changing business goals and operational conditions.
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