At the recent conf. 2025 event held in Boston, Cisco unveiled a groundbreaking suite of AI-powered observability tools that promise to revolutionize how enterprises manage and derive insights from machine-generated data. This announcement comes on the heels of Cisco’s strategic acquisition of Splunk for $28 billion, a move that has significantly bolstered its capabilities in the realm of data analytics and observability.
The core of Cisco’s new offerings revolves around the integration of advanced AI technologies into observability practices, enabling organizations to proactively monitor their digital infrastructure and applications. The suite includes a unified data fabric, a machine data lake, and a time series foundational model specifically designed for anomaly detection and root cause analysis. These innovations aim to empower enterprises to transform vast amounts of machine-generated data into actionable intelligence, thereby enhancing operational efficiency and decision-making processes.
One of the standout features of this new suite is the introduction of AI-powered observability agents. These agents are designed to operate across the entire incident response lifecycle, autonomously collecting telemetry data, identifying anomalies, and recommending fixes. By automating these processes, Cisco aims to reduce the time and effort required for troubleshooting, allowing IT teams to focus on more strategic initiatives rather than getting bogged down in reactive problem-solving.
The AI troubleshooting agent, which is part of Splunk’s Observability Cloud and AppDynamics platform, exemplifies this innovation. It autonomously analyzes incidents, identifies root causes, and provides recommendations for resolution. This capability not only accelerates the troubleshooting process but also minimizes downtime, ensuring that critical applications and services remain operational. Other agents within the suite assist teams in setting up alert correlations, summarizing alerts, and analyzing trends and impacts, further streamlining the troubleshooting workflow.
Patrick Lin, Senior Vice President and General Manager of Splunk Observability, emphasized the significance of these innovations, stating, “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.” This proactive approach to observability is crucial in today’s fast-paced digital landscape, where the ability to quickly identify and address issues can mean the difference between success and failure.
Observability itself is defined as the ability to measure a system’s internal health and performance by analyzing metrics, logs, and traces. This concept is increasingly relevant across various fields, including IT operations, software development, and business operations. As organizations continue to adopt complex digital infrastructures, the need for robust observability solutions becomes paramount.
Cisco’s announcement also highlighted the introduction of observability tools specifically designed for monitoring the performance and quality of the AI agents themselves. This self-monitoring capability ensures that the agents operate as intended, maintaining high standards of quality, security, and cost-effectiveness. By providing transparency into the performance of these AI agents, Cisco aims to build trust and reliability in its observability solutions.
In addition to the AI agents, Cisco introduced the Cisco Data Fabric, a powerful framework that enables enterprises to efficiently manage machine data at scale. Powered by the Splunk platform, the Data Fabric allows organizations to handle continuous streams of machine-generated data—such as network packet counts, CPU/GPU utilization, and error codes—effectively. This data flows continuously, often generating terabytes per day from a single enterprise environment, capturing the real-time health and performance of digital infrastructure.
The Data Fabric is designed to support various AI use cases, including training AI models and correlating multiple streams of machine and business data to generate valuable insights. Cisco’s framework facilitates data management across edge, on-premises, and cloud environments, making it a versatile solution for modern enterprises. Furthermore, it can federate data across various sources, including Amazon S3, Apache Iceberg, Delta Lake, Snowflake, and Microsoft Azure, with plans to support additional sources in the near future.
Complementing the Data Fabric is the newly announced Machine Data Lake, which provides an “AI-ready foundation” for analytics and training AI models. This data lake is essential for organizations looking to harness the power of AI in their operations, as it simplifies the process of preparing and managing machine data for analytical purposes.
Another significant component of Cisco’s announcement is the Time Series Foundation Model, a pre-trained AI model designed to perform pattern analysis and reasoning across machine data in the time series format. This model enables organizations to conduct anomaly detection, forecasting, and automated root cause analysis, further enhancing their ability to derive insights from machine-generated data. Jeetu Patel, Chief Product Officer at Cisco, noted the importance of this model, stating, “Every company has massive volumes of this machine data, but it’s been largely left out of AI for a few reasons: LLMs don’t speak the language of machine data, the information is spread across disparate silos, and the expertise and costs involved can be prohibitive. As a result, we’ve only begun to scratch the surface of what we can do with AI.”
The Time Series Foundation Model will be made available on Hugging Face, an open-source AI model repository, starting in November 2025. This accessibility is expected to encourage broader adoption of AI-driven observability practices across various industries, enabling organizations to leverage machine data more effectively.
As Cisco positions itself in the competitive landscape of AI-enabled observability, it faces formidable rivals such as New Relic, Dynatrace, Datadog, and Elastic. Each of these companies has made significant strides in the observability space, and Cisco’s latest innovations signal a strong commitment to advancing its capabilities in this area. By integrating AI into observability practices, Cisco aims to differentiate itself and provide enterprises with the tools they need to thrive in an increasingly complex digital environment.
In conclusion, Cisco’s announcement at conf. 2025 marks a pivotal moment in the evolution of observability solutions. By harnessing the power of AI and machine learning, Cisco is not only enhancing its own offerings but also setting a new standard for how organizations can manage and act on machine-generated data. As enterprises continue to navigate the challenges of digital transformation, the ability to proactively monitor and optimize their infrastructures will be crucial for success. With its innovative suite of AI-powered observability tools, Cisco is well-positioned to lead the charge in this exciting new frontier of technology.