In a significant move to enhance enterprise resilience and simplify the complexities of modern IT environments, Cisco has announced a suite of advanced AI-powered capabilities for its Splunk platform. The innovations center on agentic observability and a new Time Series Foundation Model, designed to automate and improve the detection, investigation, and resolution of issues across an organization's digital infrastructure. This announcement, made at Splunk's annual .conf25 event, leverages the recent acquisition of the data platform to introduce what Cisco is calling an "AI-native approach to observability." The new features aim to empower IT operations and engineering teams by deploying AI agents that automate telemetry collection, pinpoint root causes of problems, and recommend solutions, thereby freeing up human experts to focus on innovation rather than routine troubleshooting.[1][2][3][4]

The core of the announcement is the introduction of agentic AI innovations within the Splunk Observability portfolio, a strategy Cisco refers to as "AgenticOps."[2][4] This approach moves beyond passive monitoring to a proactive system where AI agents take on a significant portion of the incident response lifecycle.[3][5] Key among these new features are the AI Troubleshooting Agents, which are integrated into Splunk Observability Cloud and Splunk AppDynamics.[2][6] These agents automatically analyze incidents as they occur, sifting through vast amounts of data to identify potential root causes and present them to users for rapid action.[2] This capability is complemented by Event iQ in Splunk IT Service Intelligence (ITSI), which automates the correlation of alerts to reduce the constant stream of notifications that often overwhelms IT teams.[7][8] By grouping related alerts and providing consolidated summaries through a feature called ITSI Episode Summarization, the platform aims to cut through the noise and provide clear context for faster troubleshooting.[9][4]

A critical aspect of the new offering is its focus on monitoring the performance and cost of AI systems themselves. As enterprises increasingly deploy applications based on large language models (LLMs) and other AI technologies, ensuring their reliability and efficiency has become a major challenge.[7][2] To address this, Cisco has introduced AI Agent Monitoring and AI Infrastructure Monitoring. These tools provide specialized analytics to track the quality, security, and cost of LLMs and AI agents, ensuring they perform as intended and align with business goals.[2][9] This allows organizations to monitor for bottlenecks, resource consumption spikes, and other issues that could impact the performance and cost-effectiveness of their AI investments.[2] The enhanced portfolio represents a strategic unification of capabilities from Splunk AppDynamics and the Splunk Observability Cloud, creating a more cohesive experience for monitoring both traditional and modern, microservices-based environments.[7][8]

Underpinning these new agentic features is a broader initiative called the Cisco Data Fabric, which includes the announcement of a specialized Time Series Foundation Model.[1] This model is purpose-built to analyze the sequential data points that constitute the vast majority of machine-generated data in IT environments.[8][6] Set to be released on the open-source community platform Hugging Face in November 2025, the model is designed for advanced pattern analysis, anomaly detection, forecasting, and automated root cause analysis.[1][2] By understanding the temporal patterns in data, the model can proactively identify deviations from normal behavior that might indicate an impending issue.[1][8] This foundation model is a key component of the Cisco Data Fabric, an architecture powered by Splunk that aims to unify and harness machine data for AI applications without the need for costly and complex data movement.[1][3] The fabric allows for federated analysis, enabling organizations to search and analyze data where it resides across various sources like Amazon S3 and Snowflake.[1][3]

In conclusion, Cisco's latest announcements signal a clear direction for the future of IT operations, one that is heavily reliant on AI-driven automation and proactive intelligence. By integrating agentic AI capabilities directly into the Splunk platform, Cisco is aiming to transform observability from a reactive, human-intensive process into an automated, predictive function. The introduction of tools to monitor AI systems addresses a crucial and growing need in the industry, while the development of a specialized Time Series Foundation Model demonstrates a commitment to building AI that understands the specific nature of machine data. These innovations, building upon the powerful data platform provided by Splunk, position Cisco to play a significant role in helping enterprises manage the increasing complexity of their digital estates and ensure the resilience of their critical applications and services.[3][6]