Mike Vizard examines Cisco’s integration of agentic AI features and Data Fabric into its Splunk Observability platform, presenting a deeper look at how these capabilities automate IT monitoring and analysis.

Cisco Integrates AI Agents and Data Fabric into Splunk Observability Platform

Overview

Cisco announced at Splunk .Conf25 a suite of artificial intelligence (AI) agents for the Splunk Observability platform. These AI agents automate telemetry data collection using open source OpenTelemetry software, detect issues, assist in root cause analysis, and support automated fixes.

Key Highlights

  • AI Agents for Observability:
    • Automate collection of telemetry data
    • Detect IT issues and root causes
    • Apply fixes, reducing manual intervention
    • Leverage open source OpenTelemetry
  • Cisco Data Fabric:
    • Launched as a scalable platform to aggregate and analyze machine data
    • Enables analytics without pre-ingestion into a single platform
    • Will add support for time-series data analysis via multiple AI toolkits
  • Enhanced Splunk Platform Features:
    • Application Performance Management (APM) added
    • New Digital Experience Analytics (DEA) module
    • Business transaction monitoring (from AppDynamics origins)
    • Tightened integration between Splunk Real User Monitoring (RUM) and Cisco ThousandEyes
  • Agentic Collaboration and AI Canvas:
    • Demonstration of Cisco AI Canvas, an agentic AI framework promoting collaboration across network, security, and DevOps teams
    • Unified interface for real-time telemetry and AI insights

Industry Perspective

  • Cisco leaders Kamal Hathi and Jeetu Patel highlighted that AI agents significantly accelerate the convergence of IT monitoring, observability, and analytics.

  • The transition supports IT staff and automates incident response through large-scale AI agent participation, optimizing data consumption and analysis for both humans and machines.

  • According to Mitch Ashley (Futurum Group), Cisco Data Fabric helps analyze data in place, enabling more flexible and scalable observability and paving the way for deeper monitoring of AI agents and LLMs.

Strategic Impact

  • As IT environments grow more complex, AI agents facilitate greater scale in managing telemetry data and incident response, breaking down silos between IT, security, and DevOps.
  • Expanded monitoring and analytics make it easier for organizations to converge their approaches and handle the exponential growth in telemetry data.

Further Resources

Author: Mike Vizard, as featured on DevOps.com.

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