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New Relic Enhances AIOps Capabilities

New Relic enhanced New Relic AI, a suite of AIOps capabilities built for on-call DevOps, Site Reliability Engineering (SRE) and network operations center (NOC) teams responsible for operating modern infrastructure.

New Relic AI provides advanced applied intelligence (AI) and machine learning (ML) technologies to help customers detect, diagnose and resolve incidents faster, and continuously improve incident management workflow.

“New Relic's goal is to help reduce the toil and anxiety of running modern systems for engineering teams. We're proud to report that our early-access customers reported that they have seen automatic reductions in alert noise by 50 percent -- and some as much as 80 percent within days,” said Guy Fighel, GVP and Product GM at New Relic. “New Relic AI is the only solution that has the automation, intelligence and scale-out architecture needed to deliver true observability and offer precise insights that today’s modern and complex enterprises require. We continue to push the boundaries to empower DevOps and SRE teams as we enhance our platform relentlessly.”

New Relic AI delivers a holistic AIOps solution that not only understands historical alerts, but also applies machine learning and AI to significantly reduce alert noise, enrich incidents with context, and provide intelligence and automation to on-call teams in real-time. Deeply integrated with the New Relic One observability platform, New Relic AI is an open incident correlation and intelligence solution that is source and data agnostic. With unique access to NRDB, a unified telemetry database, New Relic AI fuels ML models and provides an intelligent, context-rich incident response workflow, drawing on key capabilities that include:

- Proactive Detection to detect problems earlier: Continuously evaluates telemetry data for anomalies and proactively notifies customers in their existing collaboration tools. This allows for quick action to prevent larger problems before they impact customer experience. New Relic AI enables customers to ingest, analyze, and take action on multiple data types, including alerts, logs, metrics, deployment events and more. This gives teams better context into incidents that occur and how they impact the broader environment, so they can diagnose and prioritize problems faster.

- Incident Intelligence to reduce alert noise and diagnose and respond faster: New Relic AI deeply integrates with many data sources to group related alerts and incidents and includes AI/ML-powered suggested correlations to help customers prioritize alerts and focus on the most important issues. Alert noise is automatically reduced by correlating related alerts, events, and incidents, while also suppressing flapping and low-priority alerts. Correlated incidents are enriched with context, automatically classified based on golden signals (i.e. errors, saturation, traffic, latency), as well as identifying related components affected and suggesting responders, to help on-call teams get closer to root cause and take action faster. In addition, it frees users from the steep learning curves, lengthy implementations and complex integrations typically found with other AIOps tools. By leveraging incident correlation, early access customers have reported that they have seen automatic reductions in alert noise by 50 percent.

- Deep integration with existing incident management workflows: New Relic AI integrates with Slack, PagerDuty, ServiceNow, OpsGenie, VictorOps and other tools to fit within customers’ existing incident management workflow. Enriched incidents with relevant context and ML-powered guidance and suggestions are automatically shared in team’s existing workflows, removing the need to switch between tools in times of crisis. Customers can see a live view of ingested data, an intelligent summary of each incident, and have the ability to tune correlations with user feedback.

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New Relic Enhances AIOps Capabilities

New Relic enhanced New Relic AI, a suite of AIOps capabilities built for on-call DevOps, Site Reliability Engineering (SRE) and network operations center (NOC) teams responsible for operating modern infrastructure.

New Relic AI provides advanced applied intelligence (AI) and machine learning (ML) technologies to help customers detect, diagnose and resolve incidents faster, and continuously improve incident management workflow.

“New Relic's goal is to help reduce the toil and anxiety of running modern systems for engineering teams. We're proud to report that our early-access customers reported that they have seen automatic reductions in alert noise by 50 percent -- and some as much as 80 percent within days,” said Guy Fighel, GVP and Product GM at New Relic. “New Relic AI is the only solution that has the automation, intelligence and scale-out architecture needed to deliver true observability and offer precise insights that today’s modern and complex enterprises require. We continue to push the boundaries to empower DevOps and SRE teams as we enhance our platform relentlessly.”

New Relic AI delivers a holistic AIOps solution that not only understands historical alerts, but also applies machine learning and AI to significantly reduce alert noise, enrich incidents with context, and provide intelligence and automation to on-call teams in real-time. Deeply integrated with the New Relic One observability platform, New Relic AI is an open incident correlation and intelligence solution that is source and data agnostic. With unique access to NRDB, a unified telemetry database, New Relic AI fuels ML models and provides an intelligent, context-rich incident response workflow, drawing on key capabilities that include:

- Proactive Detection to detect problems earlier: Continuously evaluates telemetry data for anomalies and proactively notifies customers in their existing collaboration tools. This allows for quick action to prevent larger problems before they impact customer experience. New Relic AI enables customers to ingest, analyze, and take action on multiple data types, including alerts, logs, metrics, deployment events and more. This gives teams better context into incidents that occur and how they impact the broader environment, so they can diagnose and prioritize problems faster.

- Incident Intelligence to reduce alert noise and diagnose and respond faster: New Relic AI deeply integrates with many data sources to group related alerts and incidents and includes AI/ML-powered suggested correlations to help customers prioritize alerts and focus on the most important issues. Alert noise is automatically reduced by correlating related alerts, events, and incidents, while also suppressing flapping and low-priority alerts. Correlated incidents are enriched with context, automatically classified based on golden signals (i.e. errors, saturation, traffic, latency), as well as identifying related components affected and suggesting responders, to help on-call teams get closer to root cause and take action faster. In addition, it frees users from the steep learning curves, lengthy implementations and complex integrations typically found with other AIOps tools. By leveraging incident correlation, early access customers have reported that they have seen automatic reductions in alert noise by 50 percent.

- Deep integration with existing incident management workflows: New Relic AI integrates with Slack, PagerDuty, ServiceNow, OpsGenie, VictorOps and other tools to fit within customers’ existing incident management workflow. Enriched incidents with relevant context and ML-powered guidance and suggestions are automatically shared in team’s existing workflows, removing the need to switch between tools in times of crisis. Customers can see a live view of ingested data, an intelligent summary of each incident, and have the ability to tune correlations with user feedback.

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Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...