Cisco has unveiled its agentic AI-powered Splunk Observability portfolio, introducing a new AI-native approach designed to redefine how enterprises manage resilience in increasingly complex digital environments. The updated platform unifies observability across hybrid and multi-cloud systems, provides actionable business insights, and deploys AI-driven agents to streamline the incident response lifecycle while continuously monitoring performance and quality. By integrating Splunk with Cisco’s broader technology ecosystem, organizations gain end-to-end visibility and correlation across networks, infrastructure, and applications enabling them to improve the reliability of their entire digital landscape.
“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.”
As agentic AI accelerates the development of applications with less human involvement, enterprises face new challenges in ensuring that AI-enabled applications and agents perform as intended. These advancements demand specialized telemetry aligned to business goals and costs. To meet this need, organizations require unified, in-context visibility across all environments to prioritize issues based on their business impact.
Advancing Cisco’s AgenticOps Vision
The enhanced Splunk Observability suite is powered by agentic AI to deliver proactive detection, investigation, and resolution. Key innovations include:
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AI Troubleshooting Agents: Available in Splunk Observability Cloud and Splunk AppDynamics, these features automatically analyze incidents, surface likely root causes, and accelerate issue resolution.
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Event iQ in Splunk IT Service Intelligence (ITSI): Enables automated alert correlation, reducing noise and providing clear context across grouped alerts.
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ITSI Episode Summarization: Offers AI-generated overviews of grouped alerts, highlighting trends, impact, and root causes to speed up troubleshooting.
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Observability for AI Applications and Infrastructure
With the rise of AI agents and large language models (LLMs), Splunk provides specialized analytics to ensure these systems operate securely, cost-effectively, and in line with business objectives. Capabilities include:
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AI Agent Monitoring: Tracks the quality, security, and cost efficiency of LLMs and AI agents to validate performance.
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AI Infrastructure Monitoring: Provides proactive alerts on bottlenecks and service spikes to optimize AI infrastructure usage and control costs.
Unified Observability Across Digital Environments
Cisco is deepening the integration of Splunk AppDynamics, Splunk Observability Cloud, and Cisco ThousandEyes to provide a holistic view of application performance and end-user experience. New enhancements include:
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Business Insights: Correlates application health with critical business processes like supply chain flows, checkout, and loan processing.
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Digital Experience Analytics: Gives product and design teams deeper visibility into user journeys and behavior for improved customer experience.
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APM Enhancements: Extends application performance monitoring (APM) across hybrid and cloud-native environments.
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Session Replay for Real User Monitoring (RUM): Helps optimize browser and mobile experiences in both AppDynamics and Splunk Observability Cloud.
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Splunk AppDynamics Agent with OpenTelemetry: Allows flexible data collection across both platforms.
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RUM Integration with Cisco ThousandEyes: Correlates real-user experience with network performance to pinpoint regions or third-party domains impacting applications.
“Through the new agentic AI innovations within Splunk Observability, Cisco offers organizations more proactive visibility and actionable insights into both their digital operations and AI system health and performance,” said Torsten Volk, Principal Analyst, Application Modernization, Enterprise Strategy Group. “These kinds of capabilities are critical as enterprises look to scale AI in a controlled and reliable manner.”