Cisco Extends AI Agent Capabilities Across IT Operations
Cisco announced new features for AgenticOps, its agent-first IT operating model, extending autonomous capabilities across networking, security, and observability. The company first introduced AgenticOps last year to help IT teams manage modern infrastructure complexity.
The update addresses a core operations challenge: teams managing distributed networks and infrastructure spend significant time on routine tasks that AI agents can handle. Cisco's approach combines its network visibility with purpose-built AI models and governance controls to automate decisions while keeping humans in oversight.
Network Operations at Scale
Cisco added autonomous troubleshooting across campus, branch, and industrial networks. The system investigates connectivity and performance issues end-to-end, reducing mean time to repair to minutes rather than hours.
New capabilities include continuous optimization that flags performance problems before users notice them, and risk-aware validation that tests network changes against live configurations to identify blast radius before deployment.
For data center networks, the system correlates events across multiple sources to surface prescriptive recommendations. Service provider customers get agentic diagnosis across multi-vendor environments, addressing a common operational friction point.
Firewall and Security Operations
Cisco Security Cloud Control now includes agentic policy recommendations. The system analyzes firewall traffic to suggest stronger zero trust controls for sensitive applications.
The platform detects performance bottlenecks-such as elephant flows consuming bandwidth-and flags them for action. A compliance feature continuously monitors firewall configurations against PCI-DSS standards and recommends fixes.
Monitoring Agent Performance
Splunk Observability Cloud added AI Agent Monitoring to track how LLM and agentic applications perform in production. The tool visualizes agent workflows and measures performance, cost, quality, and behavior.
For operations teams deploying AI agents, this addresses a practical gap: visibility into what agents are actually doing and whether they're delivering expected value.
Learn more: Operations professionals implementing AI-driven systems should explore AI Learning Path for Operations Managers and resources on AI Agents & Automation.
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