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3 minute read
The Brief: Cisco has unveiled an expanded Splunk Observability portfolio powered by agentic AI, designed to help enterprises manage digital operations with greater accuracy and efficiency. The new capabilities bring unified observability across environments, enabling organizations to connect performance data with business context while deploying AI-powered agents to manage the full incident response lifecycle. Features include automated telemetry collection, intelligent troubleshooting, event correlation, and episode summarization within Splunk IT Service Intelligence (ITSI). Additionally, specialized monitoring now extends to AI applications, large language models (LLMs), and AI infrastructure, ensuring both performance and cost alignment. The integration of Splunk AppDynamics and Splunk Observability Cloud further streamlines monitoring across traditional, cloud-native, and hybrid environments. Several new features are now generally available, while others are entering private preview.
Source: SplunkCisco Introduces Agentic AI in Splunk Observability for Real Time Insights
Analyst Perspective: Cisco’s integration of agentic AI into Splunk Observability underscores the industry’s shift toward embedding intelligence directly into operational workflows. Rather than relying solely on human intervention for monitoring and response, the platform now incorporates autonomous capabilities that enhance detection, analysis, and resolution processes. This approach helps organizations maintain reliability as digital environments become more complex and distributed.
The introduction of AI-specific observability also reflects a timely move, as enterprises increasingly deploy AI agents and large language models. These workloads require specialized monitoring to confirm performance, manage costs, and align with business objectives. Through offering a framework that addresses both application reliability and AI system oversight, Cisco positions Splunk Observability as more than just a monitoring tool—it becomes a governance layer for digital and AI-driven operations.
Expanding Observability Across Digital Environments
Cisco’s enhanced Splunk Observability portfolio is structured to provide unified visibility across applications, infrastructure, and networks. Through integrating observability functions with business context, the platform allows IT teams to prioritize issues that impact critical processes, such as customer transactions and supply chain flows. This contextual approach ensures that enterprises can respond with greater precision, connecting technical performance metrics to tangible business outcomes.
Through Splunk AppDynamics and the Splunk Observability Cloud, the system now extends its reach into hybrid and multi-cloud environments, supporting both modern microservices and traditional three-tier architectures. The result is a consolidated view of digital health, enabling teams to trace issues across complex, distributed environments. These integrations reflect Cisco’s strategy to bring together its existing technology portfolio with Splunk’s analytics capabilities, creating a foundation for proactive, data-driven operations.
Source: SplunkAI-Powered Agents in Incident Management
The introduction of AI troubleshooting agents marks an important shift in how organizations address incidents. Embedded within Splunk Observability Cloud and Splunk AppDynamics, these agents automatically analyze events, identify potential root causes, and suggest corrective actions. This automation reduces the need for manual intervention during high-pressure incidents, giving teams the capacity to act quickly and limit business disruption.
Within Splunk IT Service Intelligence (ITSI), the Event iQ feature complements this approach by filtering alert noise through automated correlation. ITSI’s new episode summarization function adds further value, generating concise overviews of grouped alerts that highlight impact, trends, and likely causes. These tools streamline the incident response lifecycle, supporting faster detection, diagnosis, and resolution. For organizations operating at scale, this model reduces operational overhead while improving the resilience of digital services.
Specialized Monitoring for AI Systems
As enterprises integrate large language models and AI agents into their workflows, observability requirements evolve. Cisco has extended Splunk’s monitoring capabilities to include the performance, cost, and security of AI systems. With AI agent monitoring, organizations can assess whether deployed models align with business objectives and operate within acceptable cost thresholds. This function addresses the growing demand for transparency in AI-driven applications, where reliability and efficiency directly influence business outcomes.
In addition, AI infrastructure monitoring provides proactive insights into the utilization of compute resources, helping teams identify bottlenecks and manage consumption. Through combining these tools, Cisco equips enterprises with the means to ensure AI services remain stable, secure, and financially sustainable. This focus on AI-specific observability positions Splunk as a platform for operational monitoring and for governing emerging AI ecosystems in enterprise environments.
Source: SplunkEnhancing the End-User and Business Experience
Cisco has also emphasized user experience and business performance through new observability capabilities. Digital Experience Analytics in Splunk Observability Cloud now allows product and design teams to evaluate customer interactions with greater depth, generating insights into user journeys and application behavior. Similarly, Business Insights connects application performance data with the health of key business processes, enabling organizations to align technical reliability with operational outcomes.
For teams monitoring hybrid environments, enhanced Application Performance Monitoring (APM) strengthens support for both cloud-native services and legacy applications. Features such as Browser and Mobile Session Replay expand visibility into real-user behavior, while integration with Cisco ThousandEyes correlates network performance with customer experience. These developments enable IT, product, and business teams to collaborate effectively, ensuring digital performance improvements are directly tied to user satisfaction and enterprise objectives.
Observability as a Foundation for AI-Driven Operations
Cisco’s latest Splunk Observability updates illustrate a clear trajectory toward embedding AI into operational practices at scale. The portfolio is evolving into a platform that monitors and governs digital and AI workloads, reflecting the complexity of modern enterprise ecosystems. Through unifying insights across infrastructure, applications, and business processes, Cisco is enabling teams to manage risk while keeping services resilient and cost-effective.
This forward-looking approach also positions observability as more than a reactive function; it becomes an essential component of enterprise strategy. As organizations balance the benefits and risks of AI adoption, tools like Splunk Observability provide the structure needed to evaluate performance, maintain control, and ensure outcomes align with business goals. For enterprises navigating distributed and AI-driven environments, Cisco’s investment in observability signals a continued push toward building trust, transparency, and operational reliability in an era defined by intelligent systems.
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