Splunk Observability Update (Q1 2026): Deeper Insights for AI Agents and Digital Experiences

In our Q1 2026 update, we’re excited to highlight the latest innovations across Splunk Observability, with a strong focus on helping teams operate confidently in an increasingly AI-powered world.

As teams begin building applications powered by LLMs and AI agents, they are encountering a new set of observability challenges that go far beyond traditional applications. To address this, we’re introducing AI agent and infrastructure monitoring to help teams monitor the performance, quality, cost, and security risks of AI-powered applications and move from experimentation to production. These releases include:

• AI Agent Monitoring in Observability Cloud

• AI Infrastructure Monitoring in Observability Cloud with new support for Cisco AI PODs

AI is also transforming how teams troubleshoot and resolve incidents. With the new Troubleshooting Agent in Observability Cloud and support for the Splunk MCP Server, we’re modernizing what has traditionally been a manual and error-prone process. The Troubleshooting Agent automatically correlates signals across metrics, events, logs, and traces, surfaces likely root causes, and recommends next steps, helping teams resolve issues faster and with greater confidence. These capabilities include:

• AI Troubleshooting Agent and Remediation Plan in Observability Cloud

• AI Troubleshooting Agents in AppDynamics

• Observability Cloud capabilities on Splunk MCP Server

Finally, meaningful AI insights depend on unified observability data to provide full context. That is why we are announcing several new capabilities that deliver deeper, end-to-end visibility across the entire application stack, from business outcomes and digital experience to applications, infrastructure, and networks. Highlights include new Digital Experience Analytics capabilities that reveal how users truly interact with your applications, as well as an ITSI content pack for Cisco Data Center Networking that helps ensure network service health across network domains.

• Digital Experience Analytics in Observability Cloud

• Secure Application in Observability Cloud

• ITSI Content Pack for Cisco Data Center Networking

• Mobile Real User Monitoring for React Native & Flutter in Splunk Observability Cloud

• Business Insights in Observability Cloud

• Native Kubernetes alerting in AppDynamics

Observability for AI

AI is everywhere, reshaping software development, customer support, and business workflows. But it has also introduced “AI slop”—inauthentic, inaccurate, low-quality, and sometimes harmful outputs that are increasingly hard to detect at scale. As AI systems become more autonomous and complex, teams must ensure models, agents, and supporting infrastructure operate reliably, safely, and cost-effectively in line with business goals. This requires observability to evolve, providing deeper context and unified visibility that correlates business outcomes with performance, quality, security, and cost metrics across the AI stack.

Monitor the performance, quality, security, and cost of LLM and agentic applications with AI Agent Monitoring in Observability Cloud (GA)

AI Agent Monitoring in Observability Cloud ensures teams can pinpoint and correlate the root cause of unreliable or degraded AI agent and model performance. Teams can evaluate responses and track performance metrics such as latency and errors alongside quality and security metrics like hallucinations, bias, drift, and accuracy, as well as cost and token usage metrics.

By tracing and mapping dependencies to analyze LLM and agentic workflows, tool calls, and other service level objectives (SLOs), teams will be able to correlate and improve model quality and behavior with business impact to build trust and reliability.

Finally, Cisco AI Defense will be integrated with AI Agent Monitoring in Observability Cloud so that teams can comply with AI standards, and detect and mitigate AI (LLM, agents and tools) risks, misuse, drifts, leakage, and threats like personally identifiable information (PII) leakage, prompt injection, or policy violations in real time.

AI Agent Monitoring in Observability Cloud is now generally available (GA). It’s also available in AppDynamics for on-premises customers. Set up AI Agent Monitoring today or explore the product with an interactive tour.

Monitor AI Infrastructure health, availability, and consumption in Observability Cloud, including Cisco AI PODs (GA)

Cisco AI PODs are pre-validated, full-stack data center infrastructure solutions designed to support the entire AI lifecycle, including training, fine-tuning, and inferencing workloads. AI Infrastructure Monitoring provides teams with data-dense dashboards and detectors for orchestration frameworks, agents, model providers, vector databases, GPUs, and more to surface trends in underlying AI infrastructure performance.

In November 2025, we added support to monitor Cisco AI PODs, as well as Nvidia NIMs, Milvus and Pinecone vector databases, LiteLLM proxy services, GCP VertexAI applications, and more. These views allow teams to track “tokenomics” metrics like time-to-first token and estimated token costs, and operational metrics such as throughput and GPU and memory utilization from AI apps and services to manage costs, optimize resources, and detect performance degradation. By visualizing the business impact of AI components, teams will be able to assess the utilization and efficiency of their Cisco AI PODs and other hosted AI infrastructure. AI Infrastructure Monitoring is generally available (GA). Set up AI Infrastructure Monitoring today and read this blog to learn more about AI Infrastructure Monitoring for Cisco AI PODs.

AI-powered Observability

Historically, incident response was reactive, forcing teams to troubleshoot across siloed systems and manually correlate overwhelming volumes of telemetry data. This made root cause analysis slow, labor-intensive, and hard to scale. As modern teams ship faster into increasingly complex, AI-powered environments, they need troubleshooting workflows that are faster, more accurate, and repeatable.

Investigate and troubleshoot faster with AI agents

AI troubleshooting agent and remediation plan in Observability Cloud

Rather than sifting through dashboards, searching for relevant application and infrastructure metrics or logs, or switching between multiple screens, tabs, and tools to piece together possible causes, the AI troubleshooting agent and remediation plan in Observability Cloud is a fellow SRE that pinpoints evidence-based root cause. When an alert triggers, the troubleshooting agent analyzes metrics, events, logs, and traces to generate suspected root causes, evidence-backed summaries of the immediate impact of the incident on affected services and user sessions, and a human-verified action plan for long-term resolution. This is presented in an interactive manner to ensure on-call engineers understand what happened and how to fix it—all in plain language.

Whether you’re a new engineer joining the team, a service owner who cares about business impact, or an on-call engineer, the interactive workflow will enable you to understand root causes and remediation in a timely manner.

AI troubleshooting agents in AppDynamics

When anomalies or health rule violations occur in an n-tier or hybrid environment, the AI troubleshooting agent for AppDynamics goes beyond simply flagging an issue. It explains what’s happening, why it’s happening, and how to fix it. Like the AI troubleshooting agent and remediation plan in Observability Cloud, teams can receive clear, concise root cause summaries in AppDynamics right where you need them, without bouncing between dashboards or guessing which metric matters most.

The result? Teams resolve issues faster and with greater accuracy, even if they aren’t system experts. It removes knowledge barriers, guides investigations with just one click, and presents actionable insights in the context of real-time data.

Read this blog to learn more.

Connect your AI Agents to Observability Cloud capabilities on Splunk MCP Server (Alpha)

AI agents now have one unified MCP server to access Splunk’s tool capabilities. Splunk MCP Server provides a secure and scalable interface for connecting AI assistants, agents, and other intelligent systems to Splunk data, and now specifically to Observability Cloud capabilities. This provides teams with the flexibility to seamlessly integrate their local AI agents, LLMs, and tools, and data with Observability Cloud data to build custom AI workflows and debug performance issues in production without leaving their environment. Observability Cloud capabilities on Splunk MCP Server are in Alpha. Learn more about Splunk MCP Server in our documentation.

Unified Observability Experience

Modern systems span applications, infrastructure, networks, and cloud services, but observability data often remains fragmented across tools and teams. These silos make troubleshooting slow and manual, forcing engineers to piece together partial signals without a complete picture. At the same time, effective AI-driven insights depend on unified, high-quality data with full context. A unified observability experience brings these signals together, enabling faster root cause analysis and powering AI that can deliver accurate, actionable guidance.

Unify user behavior and observability data with Digital Experience Analytics in Observability Cloud (GA)

To deliver great digital experiences, teams need visibility from every angle, combining traditional observability signals like latency, JavaScript errors, and load times with insights into user behavior, including feature adoption, friction points, and drivers of drop-offs or conversions.

Digital Experience Analytics (DEA) in Splunk Observability Cloud provides deep, intuitive insight into user behavior across digital products. By unifying behavioral data with observability signals from real user monitoring (RUM) and application performance monitoring (APM), Splunk helps Product, UX, and Engineering teams to pinpoint friction, increase conversion rates, and prioritize engineering work more effectively. All of this is powered through a single, lightweight OpenTelemetry instrumentation agent for easy setup and minimal overhead.

Digital Experience Analytics is now generally available for Observability Cloud. Learn more in the blog or explore the documentation to start using it today.