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Cisco Expands AgenticOps Innovations Across Portfolio

Cisco announced new AgenticOps innovations for the AI era. 

First launched last year, AgenticOps is an agent-first IT operating model for autonomous action with built-in oversight. New capabilities unveiled today across networking, security, and observability further transform how IT teams operate at scale.  

Cisco’s AgenticOps provides the foundation to absorb operational complexity and operate effectively at scale.

“For teams responsible for operating and securing distributed networks and infrastructure, AgenticOps represents a profound and fundamental shift away from complexity,” said Jeetu Patel, President and Chief Product Officer, Cisco. “This is the true power of Cisco as a platform. By delivering agentic capabilities aligned to critical IT operations priorities, we’re combining Cisco’s unique cross‑domain visibility, purpose-built models, and governance together to supercharge teams.”

Last year, Cisco introduced AgenticOps and redefined how AI is applied in networking to manage the growing complexity of modern IT operations. Powered by advanced AI and unified network data, including the Deep Network Model, solutions like Agentic Workflows and AI Canvas help IT teams troubleshoot faster and automate securely. Now, Cisco is extending agentic-driven operations across networking, security, and observability, delivering AgenticOps to support IT operations in cloud, on‑premises, air‑gapped industrial, enterprise, data center, and service provider environments.  

Cisco’s AgenticOps is informed by system‑wide awareness drawn from one of the industry’s richest sources of cross‑domain telemetry across Cisco Networking, Security Cloud Control, Cisco Nexus One, Splunk, and more. By ingesting live signals from owned and unowned networks, security controls, applications, and collaboration platforms, including Cisco ThousandEyes, Secure Firewall, and Splunk Observability, AgenticOps delivers context‑aware, agentic execution at real‑world operational scale. The result is trusted, closed‑loop execution that shifts day‑to‑day operations from humans to machines, while keeping teams firmly in control of outcomes.

New tools, skills, and platform enhancements include:

  • Autonomous Troubleshooting: End-to-end agentic investigations across campus, branch, and industrial networks triage connectivity and experience issues, cutting MTTR to minutes. Applies reasoning from telemetry to root cause, validating multiple hypotheses simultaneously and executing deterministic remediations with CCIE-grade precision.
  • Continuous Optimization: Context-aware agentic recommendations to prevent performance degradation before users feel it. Continuously maintains user experience by autonomously tuning RF, QoS, path, and control planes with a live understanding of end-to-end network conditions.
  • Trusted Validation: Risk-aware agentic assessments validate network changes against live topology, configuration, and telemetry, including identifying impact and blast radius. Leverages deep reasoning to perform complex tasks such as compliance validation.
  • Experience Metrics: Transforms thousands of network signals into a single view focused on clear, actionable metrics for user experience, such as Time to Connect, Capacity, and Roaming.
  • Agentic Workflow Creation: Create production-ready, deterministic automations within Cisco AI Assistant for custom, repeatable, and verifiable workflows based on environment conditions.
  • Agentic capabilities for Campus, Branch, and Industrial will start rolling out February 2026.
  • Data Center: Early detection and intelligent event correlation with AgenticOps for data center networks enables the delivery of prescriptive recommendations to optimize performance. By providing actionable insights across traditional and AI workloads, the solution drives proactive operations and significantly improves business outcomes. This capability enhances the observability and unified operations of Cisco Nexus One. Controlled availability in June 2026.
  • Service Provider: Accelerating the journey to autonomous networking, agentic capabilities in Crosswork AI identify, diagnose, and resolve complex, multi‑vendor issues in service provider networks with greater speed, accuracy, and confidence. Now in beta.
  • Tracking the performance, cost, quality, and behavior of LLM and agentic applications, AI Agent Monitoring in Splunk Observability Cloud visualizes agent workflows and will soon integrate with Cisco AI Defense to mitigate risks that inhibit trust in AI models, such as bias, hallucinations, data leakage, and prompt injection. Generally available February 25. 

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

Cisco Expands AgenticOps Innovations Across Portfolio

Cisco announced new AgenticOps innovations for the AI era. 

First launched last year, AgenticOps is an agent-first IT operating model for autonomous action with built-in oversight. New capabilities unveiled today across networking, security, and observability further transform how IT teams operate at scale.  

Cisco’s AgenticOps provides the foundation to absorb operational complexity and operate effectively at scale.

“For teams responsible for operating and securing distributed networks and infrastructure, AgenticOps represents a profound and fundamental shift away from complexity,” said Jeetu Patel, President and Chief Product Officer, Cisco. “This is the true power of Cisco as a platform. By delivering agentic capabilities aligned to critical IT operations priorities, we’re combining Cisco’s unique cross‑domain visibility, purpose-built models, and governance together to supercharge teams.”

Last year, Cisco introduced AgenticOps and redefined how AI is applied in networking to manage the growing complexity of modern IT operations. Powered by advanced AI and unified network data, including the Deep Network Model, solutions like Agentic Workflows and AI Canvas help IT teams troubleshoot faster and automate securely. Now, Cisco is extending agentic-driven operations across networking, security, and observability, delivering AgenticOps to support IT operations in cloud, on‑premises, air‑gapped industrial, enterprise, data center, and service provider environments.  

Cisco’s AgenticOps is informed by system‑wide awareness drawn from one of the industry’s richest sources of cross‑domain telemetry across Cisco Networking, Security Cloud Control, Cisco Nexus One, Splunk, and more. By ingesting live signals from owned and unowned networks, security controls, applications, and collaboration platforms, including Cisco ThousandEyes, Secure Firewall, and Splunk Observability, AgenticOps delivers context‑aware, agentic execution at real‑world operational scale. The result is trusted, closed‑loop execution that shifts day‑to‑day operations from humans to machines, while keeping teams firmly in control of outcomes.

New tools, skills, and platform enhancements include:

  • Autonomous Troubleshooting: End-to-end agentic investigations across campus, branch, and industrial networks triage connectivity and experience issues, cutting MTTR to minutes. Applies reasoning from telemetry to root cause, validating multiple hypotheses simultaneously and executing deterministic remediations with CCIE-grade precision.
  • Continuous Optimization: Context-aware agentic recommendations to prevent performance degradation before users feel it. Continuously maintains user experience by autonomously tuning RF, QoS, path, and control planes with a live understanding of end-to-end network conditions.
  • Trusted Validation: Risk-aware agentic assessments validate network changes against live topology, configuration, and telemetry, including identifying impact and blast radius. Leverages deep reasoning to perform complex tasks such as compliance validation.
  • Experience Metrics: Transforms thousands of network signals into a single view focused on clear, actionable metrics for user experience, such as Time to Connect, Capacity, and Roaming.
  • Agentic Workflow Creation: Create production-ready, deterministic automations within Cisco AI Assistant for custom, repeatable, and verifiable workflows based on environment conditions.
  • Agentic capabilities for Campus, Branch, and Industrial will start rolling out February 2026.
  • Data Center: Early detection and intelligent event correlation with AgenticOps for data center networks enables the delivery of prescriptive recommendations to optimize performance. By providing actionable insights across traditional and AI workloads, the solution drives proactive operations and significantly improves business outcomes. This capability enhances the observability and unified operations of Cisco Nexus One. Controlled availability in June 2026.
  • Service Provider: Accelerating the journey to autonomous networking, agentic capabilities in Crosswork AI identify, diagnose, and resolve complex, multi‑vendor issues in service provider networks with greater speed, accuracy, and confidence. Now in beta.
  • Tracking the performance, cost, quality, and behavior of LLM and agentic applications, AI Agent Monitoring in Splunk Observability Cloud visualizes agent workflows and will soon integrate with Cisco AI Defense to mitigate risks that inhibit trust in AI models, such as bias, hallucinations, data leakage, and prompt injection. Generally available February 25. 

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.