The observability landscape has evolved dramatically over the past decade. What began as simple monitoring has transformed into comprehensive observability platforms that ingest massive volumes of telemetry data from increasingly complex, hybrid infrastructure environments. Despite these advances, organizations face a persistent challenge: the gap between data collection and actionable insight continues to widen. As infrastructure scales and diversifies across on-premises, cloud, and edge environments, the cognitive load on IT operations teams has become unsustainable.
This is where the application of AI agents and LLMs to IT operations becomes not just valuable, but essential. With the public preview of AI Assistant in NetApp Data Infrastructure Insights (DII), we're seeing a practical implementation of AIOps that addresses real-world operational challenges in meaningful ways.
The Observability Data Paradox
DII collects and aggregates metrics, logs, and events from potentially thousands of infrastructure components across multi-vendor, multi-cloud environments. This includes comprehensive telemetry from storage systems, compute resources, and virtualization layers, creating a unified view of your entire infrastructure stack. However, this wealth of data creates its own problem. Traditional observability tools provide the data points, but connecting those dots requires deep domain expertise, custom tooling, and significant time investment which evaporates quickly during critical incidents.
When an issue arises, operations teams must consider signals across multiple dashboards, query various data sources, understand complex dependencies, and mentally map relationships between infrastructure layers. A storage latency spike might stem from network congestion, a misconfigured QoS policy, an unexpected workload pattern, or resource contention three layers removed from the symptom.
DII already alleviates much of that burden with its inherent correlation, heterogeneous normalization, and analysis capabilities but there is still a learning curve to take advantage of all of the capabilities the product offers.
The AIOps Value Proposition in Observability
AIOps fundamentally changes this paradigm by applying machine learning, natural language processing, and advanced analytics directly to observability data. Rather than presenting raw metrics and expecting humans to synthesize insights, AI can actively analyze patterns, detect anomalies, correlate events, and surface root causes automatically.
The value manifests across several dimensions:
Accelerated Mean Time to Resolution (MTTR): By automating correlation analysis and root cause identification, AI dramatically reduces the time from symptom detection to problem resolution. What might take an experienced engineer 30-60 minutes of investigation can be surfaced in seconds.
Democratized Expertise: Not every team member possesses deep knowledge of storage protocols, network topology, and application architecture. AI encodes expert knowledge into algorithms, making sophisticated analysis accessible to broader teams.
Proactive Operations: Beyond reactive troubleshooting, AI enables predictive analytics; identifying trends toward capacity exhaustion, performance degradation, or configuration drift before they impact production workloads.
Context Preservation: During incidents, AI maintains context across the entire infrastructure stack, preventing the tunnel vision that often occurs when teams focus narrowly on individual components.
AI Assistant: Purpose-Built for Data Infrastructure Insights
The AI Assistant in Data Infrastructure Insights represents a thoughtful implementation of AIOps principles, specifically designed for the complexities of hybrid infrastructure environments. Rather than bolting AI capabilities onto an existing platform as an afterthought, AI Assistant is deeply integrated into DII's architecture, leveraging the platform's comprehensive data collection and unified data model.
Natural Language as the Interface
The most immediately apparent capability is natural language processing. Instead of constructing complex queries, navigating dashboard hierarchies, or writing custom scripts, operators can simply ask questions in plain English:
"What's causing high latency in my SQL database?"
This seemingly simple query triggers sophisticated processing. AI Assistant must parse the intent, identify the relevant database instances, retrieve current and historical performance metrics, analyze latency patterns, examine the full I/O path from application through compute and network to storage, correlate timing with configuration changes or workload shifts, and synthesize findings into a coherent explanation.
The NLP layer handles ambiguity and context. "My SQL database" might refer to a specific instance the user recently viewed, a production cluster associated with their team, or require clarification. The system leverages 30+ years of NetApp expertise in the form of knowledge base and run books, and understands infrastructure terminology, vendor-specific nomenclature, and operational concepts.
Intelligent Correlation and Root Cause Analysis
Behind the conversational interface lies the true technical sophistication: automated correlation analysis across the infrastructure stack. When investigating a performance anomaly, AI Assistant doesn't examine metrics in isolation. It benefits from DII’s advanced correlation engine to paint the complete picture from the storage volumes, LUNs, and pools to the network paths and switches to the compute hosts, VMs, applications, and workloads.
DII supplies AI Assistant with temporal correlation, identifying changes that coincide with the performance degradation: configuration modifications, workload pattern shifts, resource allocation changes, or environmental factors. Machine learning models trained on historical patterns distinguish between normal operational variance and genuine anomalies, reducing false positives that plague threshold-based alerting.
Consider the prompt: "Correlate the recent spike in read IOPS on filer-01 with any recent configuration changes."
This requires AI Assistant to identify the IOPS spike timing, query the configuration management database for changes within the relevant timeframe, understand which configuration parameters could impact read performance, assess the magnitude of correlation, and present findings with justification. This multi-step analytical process happens in seconds, delivering insights that could otherwise require extensive manual investigation.
Topology-Aware Analysis
AI Assistant's integration with DII's data collectors provides topology awareness, understanding not just individual components but their relationships and dependencies. This proves critical for hybrid environments where workloads span multiple infrastructure layers and vendors.
When asked "Are there any hosts that have lost path redundancy to their storage?", AI Assistant must understand multipathing configurations, identify expected redundancy levels, detect path failures, assess impact on data availability, and prioritize findings by criticality. This requires comprehensive topology mapping and continuous state monitoring across the infrastructure.
Predictive and Proactive Capabilities
Beyond reactive troubleshooting, AI Assistant enables proactive operations through predictive analytics. The prompt "Identify all storage pools with less than 15% free space and forecast their exhaustion date" demonstrates this capability.
The system identifies pools meeting the capacity threshold, analyzes historical growth rates, accounts for workload seasonality and trends, projects future consumption, and calculates exhaustion dates. This transforms capacity planning from periodic manual reviews to continuous, automated monitoring with actionable forecasts.
The Integration Advantage
A critical differentiator is that AI Assistant isn't a standalone tool requiring separate data ingestion, configuration, or learning. It leverages DII's existing collectors, unified data model, and historical data repository. This tight integration means AI Assistant immediately understands your environment's topology, has access to comprehensive historical context, and can correlate across all monitored infrastructure components without additional setup.
For organizations already invested in Data Infrastructure Insights, AI Assistant represents an intelligence layer that amplifies existing observability investments rather than requiring parallel tooling.
The Path Forward
As hybrid infrastructure continues to grow in complexity, the gap between human cognitive capacity and operational demands will only widen. AIOps capabilities like AI Assistant represent not just an efficiency improvement but a fundamental shift in how we interact with observability data; from passive dashboards to active dialogue, from reactive investigation to proactive insight.
Data Infrastructure Insights already empowers teams to manage complex, multi-vendor environments with unified visibility and actionable insights. With AI-enhanced observability, the platform becomes even more powerful, enabling faster incident resolution, broader expertise accessibility, and proactive issue prevention. By turning complex data into clear, actionable guidance, DII ensures more reliable operations while making infrastructure management easier than ever.
Jump into Data Infrastructure Insights today and start interacting with AI Assistant. We’re eager to see how it empowers you to simplify operations and make smarter, faster decisions grounded in comprehensive insights. See it in action with this brief video demo.
Not a DII customer? Request a personalized demo to see what Data Infrastructure Insights and AI Assistant can do for you.
