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Cisco Introduces Agentic AI-Powered Splunk Observability

Cisco announced agentic AI-powered Splunk Observability, an AI-native approach to observability that sets a new standard for how customers can strengthen their resilience. 

The enhanced Splunk Observability portfolio unifies observability across environments, surfaces actionable business context, and deploys AI-powered agents across the full incident response lifecycle, while monitoring both its performance and quality. Through integrations across Cisco technologies with Splunk, customers gain unmatched visibility and correlation of data insights across their networks, infrastructure, and applications to improve the reliability of their entire digital estate.

“Our mission is clear – to help organizations put AI applications and agents to work, while retaining visibility and control,” said Patrick Lin, SVP and GM of Splunk Observability. “With the latest innovations in Splunk Observability, we are empowering enterprises to proactively monitor their critical applications and digital services with ease, resolve issues before they escalate, and ensure the value and outcomes they derive from observability are commensurate with the cost.”

Splunk is advancing Cisco's Agenticops vision through an enhanced Splunk Observability portfolio, supercharged by new agentic AI innovations. These innovations will deploy AI agents to automate telemetry collection and alert configuration, detect issues, identify root causes, and recommend fixes – freeing ITOps and engineering teams to focus on innovation. These advancements include:

  • AI Troubleshooting Agents: Offered in Splunk Observability Cloud and Splunk AppDynamics, these agentic AI features automatically analyze incidents and surface potential root causes, helping users to quickly act on issues.
  • Event iQ: Offered in Splunk IT Service Intelligence (ITSI), Event iQ helps teams easily set up automated alert correlation to quickly reduce alert noise and gain clear context on grouped alerts.
  • ITSI Episode Summarization: In conjunction with Al-driven alert correlation through Event iQ, Episode Summarization in Splunk ITSI automatically provides overviews of grouped alerts, including trends, impact and root cause, to help troubleshoot faster.

Splunk helps teams proactively monitor the health, security, and cost of their AI application stack, including agents, LLMs, and AI Infrastructure, with:

  • AI Agent Monitoring: Monitors the quality, security, and cost of LLMs and AI agents to determine whether models are performing at the right price and as intended, to align with business goals.
  • AI Infrastructure Monitoring: Proactively monitors the health and consumption of AI infrastructure by alerting on bottlenecks and spikes across services to manage costs.

Cisco is bringing the best of Splunk AppDynamics and Splunk Observability Cloud together to provide a unified experience across three-tier and microservices environments, and deepening integration with Cisco ThousandEyes so ITOps, NetOps and Engineering teams can pinpoint the network's impact on application performance and end-user experience. The innovations include:

  • Business Insights in Splunk Observability Cloud: Teams can correlate application performance with the real-time health of critical business processes, such as checkout, loan processing, and supply chain flows with minimal setup.
  • Digital Experience Analytics in Splunk Observability Cloud: Product and design teams can gain deep visibility into user journeys and behavior, accessing richer customer experience insights and a faster setup.
  • APM support for hybrid apps and business transactions in Splunk Observability Cloud: These capabilities strengthen APM for cloud-native applications and extend support for hybrid environments—building on Splunk AppDynamics' expertise in monitoring traditional three-tier applications.
  • Session Replay for Real User Monitoring (RUM) for Splunk AppDynamics and Splunk Observability Cloud: New Browser and Mobile Session Replay in Splunk AppDynamics and Splunk Observability Cloud will help teams optimize online experiences.
  • Splunk AppDynamics Agent: Leveraging OpenTelemetry, this agent enables customers to collect data in either Splunk AppDynamics or Splunk Observability Cloud, enabling Splunk AppDynamics customers to use the observability offering that suits their needs.
  • Splunk Observability Cloud Real User Monitoring (RUM) Integration with Cisco ThousandEyes: Users can correlate real-user experience with network performance across owned and third-party domains, to help pinpoint regions or services affected by network bottlenecks.

Splunk AI Agent Monitoring, AI Troubleshooting Agents, ITSI Episode Summarization, Business Insights, Digital Experience Analytics, and Splunk RUM Integration with Cisco ThousandEyes are available or will be available soon in Alpha (private preview).

All other innovations listed are now generally available to all global regions.

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

Cisco Introduces Agentic AI-Powered Splunk Observability

Cisco announced agentic AI-powered Splunk Observability, an AI-native approach to observability that sets a new standard for how customers can strengthen their resilience. 

The enhanced Splunk Observability portfolio unifies observability across environments, surfaces actionable business context, and deploys AI-powered agents across the full incident response lifecycle, while monitoring both its performance and quality. Through integrations across Cisco technologies with Splunk, customers gain unmatched visibility and correlation of data insights across their networks, infrastructure, and applications to improve the reliability of their entire digital estate.

“Our mission is clear – to help organizations put AI applications and agents to work, while retaining visibility and control,” said Patrick Lin, SVP and GM of Splunk Observability. “With the latest innovations in Splunk Observability, we are empowering enterprises to proactively monitor their critical applications and digital services with ease, resolve issues before they escalate, and ensure the value and outcomes they derive from observability are commensurate with the cost.”

Splunk is advancing Cisco's Agenticops vision through an enhanced Splunk Observability portfolio, supercharged by new agentic AI innovations. These innovations will deploy AI agents to automate telemetry collection and alert configuration, detect issues, identify root causes, and recommend fixes – freeing ITOps and engineering teams to focus on innovation. These advancements include:

  • AI Troubleshooting Agents: Offered in Splunk Observability Cloud and Splunk AppDynamics, these agentic AI features automatically analyze incidents and surface potential root causes, helping users to quickly act on issues.
  • Event iQ: Offered in Splunk IT Service Intelligence (ITSI), Event iQ helps teams easily set up automated alert correlation to quickly reduce alert noise and gain clear context on grouped alerts.
  • ITSI Episode Summarization: In conjunction with Al-driven alert correlation through Event iQ, Episode Summarization in Splunk ITSI automatically provides overviews of grouped alerts, including trends, impact and root cause, to help troubleshoot faster.

Splunk helps teams proactively monitor the health, security, and cost of their AI application stack, including agents, LLMs, and AI Infrastructure, with:

  • AI Agent Monitoring: Monitors the quality, security, and cost of LLMs and AI agents to determine whether models are performing at the right price and as intended, to align with business goals.
  • AI Infrastructure Monitoring: Proactively monitors the health and consumption of AI infrastructure by alerting on bottlenecks and spikes across services to manage costs.

Cisco is bringing the best of Splunk AppDynamics and Splunk Observability Cloud together to provide a unified experience across three-tier and microservices environments, and deepening integration with Cisco ThousandEyes so ITOps, NetOps and Engineering teams can pinpoint the network's impact on application performance and end-user experience. The innovations include:

  • Business Insights in Splunk Observability Cloud: Teams can correlate application performance with the real-time health of critical business processes, such as checkout, loan processing, and supply chain flows with minimal setup.
  • Digital Experience Analytics in Splunk Observability Cloud: Product and design teams can gain deep visibility into user journeys and behavior, accessing richer customer experience insights and a faster setup.
  • APM support for hybrid apps and business transactions in Splunk Observability Cloud: These capabilities strengthen APM for cloud-native applications and extend support for hybrid environments—building on Splunk AppDynamics' expertise in monitoring traditional three-tier applications.
  • Session Replay for Real User Monitoring (RUM) for Splunk AppDynamics and Splunk Observability Cloud: New Browser and Mobile Session Replay in Splunk AppDynamics and Splunk Observability Cloud will help teams optimize online experiences.
  • Splunk AppDynamics Agent: Leveraging OpenTelemetry, this agent enables customers to collect data in either Splunk AppDynamics or Splunk Observability Cloud, enabling Splunk AppDynamics customers to use the observability offering that suits their needs.
  • Splunk Observability Cloud Real User Monitoring (RUM) Integration with Cisco ThousandEyes: Users can correlate real-user experience with network performance across owned and third-party domains, to help pinpoint regions or services affected by network bottlenecks.

Splunk AI Agent Monitoring, AI Troubleshooting Agents, ITSI Episode Summarization, Business Insights, Digital Experience Analytics, and Splunk RUM Integration with Cisco ThousandEyes are available or will be available soon in Alpha (private preview).

All other innovations listed are now generally available to all global regions.

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...