
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
IT organizations have historically measured success by how quickly they can respond when something goes wrong. The entire discipline of Incident Management has been optimized around mean time to resolution, first-response SLAs and ticket closure rates. But new research suggests that even though this is a well-executed playbook, it's no longer enough to retain customers ...
Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...
Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...
Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...
While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...
For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...
As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...
The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...
The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...
Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...