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Response intelligence with New Relic AI

preview

We're still working on this feature, but we'd love for you to try it out!

If you have questions or feedback, or if you need help during the preview of the .NET agent's Instrumentation editor, send an email to dotnet-li-editor-beta@newrelic.com.

This feature is currently provided as part of a preview program pursuant to our pre-release policies.

The Issue page, now integrated with New Relic AI, offers real-time insights to help reduce the mean time to resolution (MTTR) of issues and incidents. It brings together the key details of an incident into a single view, enabling you to quickly understand the context of an issue without navigating through multiple screens.

A screenshot of the alerts issues feed.

The AI-powered issue page provides a concise summary that includes the affected entity, the severity of the issue, an explanation of the alert condition, and additional details to assist with debugging. Additionally, the issue page includes a new ‘overview’ tab with three widgets that address the most critical questions first responders ask when dealing with an issue.

What’s impacted?
First responders need to assess the "blast radius" to determine the severity of an issue and decide on the next steps. This widget provides an overview of the affected entities along with the impact to the end users of the application or service.

What happened previously?
Many IT issues tend to reoccur. Knowing if an issue has happened before, why it occurred, and how it was resolved can save first responders valuable time during an incident. To support this, customers can use the widget to link their existing retrospective or postmortem documents. By leveraging retrieval augmented generation (RAG), the New Relic AI platform will index and store this information for future contextual reference. Once configured, first responders will see a summary of similar past issues, along with links to the retrospective documents for detailed analysis.

What to check?
First responders often need contextual guidance on immediate actions to mitigate an issue. This widget provides customized steps to help them quickly restore services to normal operational levels. Additionally, the Potential causes tab identifies likely causes through causal analysis, covering a range of possible anomalies and performance issues. For more information, refer to casual analysis.

Causal analysis

The causal analysis engine identifies potential symptoms that might have triggered an alert event and suggests immediate mitigating actions to address them.

Consider a scenario where a PHP application encounters a memory leak, leading to a failure in the throughput SLI and triggering an alert. Our engine investigates by moving from the service level to the APM application and then to the infrastructure container to detect the symptom.

How does the engine work? The causal analysis engine uses distinct analysis categories, such as deployment events, infrastructure resource limits, and more. Each category is designed to address various potential sources of anomalies and performance issues. These categories focus on specific data types and metrics, enabling precise analysis and more accurate identification of causal relationships.

At the moment, New Relic only supports causal analysis for APM entities.

A screenshot of the alerts issues feed.

Mitigating actions & visualizations

For every identified potential cause, the engine offers tailored mitigation actions that guide users through the necessary steps to quickly restore services and entities to their normal operational states. We recognize that many of our customers typically rely on NRQL to analyze significant queries, hence we provide relevant visuals alongside the underlying query for each cause.

New Relic AI generated analysis

In some scenarios, our causal engine may not identify an algorithm-driven cause. However, we have insights that, when combined with with LLMs, can offer you actionable steps. To access this capability, you must have the New Relic AI entitlement enabled.

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