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New Relic Applied Intelligence Updated

New Relic launched new capabilities in New Relic Applied Intelligence to help engineers detect, understand, and resolve incidents faster than ever.

This latest update to New Relic One allows engineers to uncover anomalies automatically, now enabled by default and available for free to all users. Engineers can now also see the probable root cause of every incident from any data source automatically, with guidance on suggested responders on their team who may be best equipped to revolve each issue. Also available in public beta, engineers can quickly spot patterns and outliers in all of their log data using machine learning (ML) to dramatically reduce troubleshooting time.

“AIOps has promised engineers the ability to harness AI and machine learning to predict possible issues, determine root causes, and intelligently drive automation to resolve them,” said Bill Staples, President & CPO at New Relic. “Despite the hype, many DevOps and SRE teams have struggled to achieve the value of AIOps, as steep learning curves, long implementation and training times, prohibitive pricing, and lack of confidence in AI and machine learning have stood in the way. With our next-gen AIOps capabilities launched today, New Relic is solving these challenges, putting the power of observability in the hands of every engineer to finally deliver the promised value of AIOps to everyone.”

The modern capabilities now available in New Relic Applied Intelligence are designed to deliver on the promise of AIOps with speed of deployment, out of the box integrations, ease of use, and simplicity to help engineers quickly and easily:

- Detect unusual changes instantly: Automatically spot anomalies based on golden signals like throughput, errors, and latency across all applications, services, and log data. Engineers get notified in Slack and other collaboration tools, and can troubleshoot faster with in-depth anomaly analytics to detect potential problems early, before they impact customers.

- Cut down alert noise from any source: Instead of alert storms across multiple tools, events are auto-correlated based on time, context from alert messages, and now relationship data across systems so engineers see one issue with all the data needed to take action. Pre-trained ML models accelerate speed to value by eliminating steep and costly learning curves.

- Get to root cause faster: Eliminate guesswork and solve problems faster with automatic insights into the probable root cause for incidents. Engineers can quickly see why each open issue occurred, which services and systems are impacted, and what action is needed for resolution. They get ML-based guidance on suggested responders on their team who may be best equipped to revolve each issue.

- Detect patterns and outliers in log data: Machine learning detects patterns and outliers in log data to reduce troubleshooting time. Engineers can explore millions of log messages with a single click and reduce manual querying by automatically clustering their log data to quickly find anomalous patterns and problematic needles in the haystack. Because New Relic uniquely enables teams to instrument all telemetry data from any source in one place, log patterns are stored in New Relic's Telemetry Data Platform as events. This enables engineers to easily create dashboards, alerts, and queries based on log patterns for faster rollup analysis and troubleshooting of trends in their log data.

- Integrate seamlessly with PagerDuty and other popular incident management tools: Eliminate the toil of managing incidents across tools via a new integration that synchronizes the state of correlated issues in New Relic bi-directionally with PagerDuty and other popular incident management tools. As the state of correlated issues changes in New Relic and these platforms, they are all now automatically updated to help on-call engineers manage and resolve incidents more efficiently and effectively.

New Relic’s new AIOps capabilities are generally available today to all New Relic Applied Intelligence customers.

Anomaly detection is available now and enabled for all customers at no additional charge, including New Relic free tier users.

Log Patterns is now available in public beta.

The Latest

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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 ...

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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 ...

New Relic Applied Intelligence Updated

New Relic launched new capabilities in New Relic Applied Intelligence to help engineers detect, understand, and resolve incidents faster than ever.

This latest update to New Relic One allows engineers to uncover anomalies automatically, now enabled by default and available for free to all users. Engineers can now also see the probable root cause of every incident from any data source automatically, with guidance on suggested responders on their team who may be best equipped to revolve each issue. Also available in public beta, engineers can quickly spot patterns and outliers in all of their log data using machine learning (ML) to dramatically reduce troubleshooting time.

“AIOps has promised engineers the ability to harness AI and machine learning to predict possible issues, determine root causes, and intelligently drive automation to resolve them,” said Bill Staples, President & CPO at New Relic. “Despite the hype, many DevOps and SRE teams have struggled to achieve the value of AIOps, as steep learning curves, long implementation and training times, prohibitive pricing, and lack of confidence in AI and machine learning have stood in the way. With our next-gen AIOps capabilities launched today, New Relic is solving these challenges, putting the power of observability in the hands of every engineer to finally deliver the promised value of AIOps to everyone.”

The modern capabilities now available in New Relic Applied Intelligence are designed to deliver on the promise of AIOps with speed of deployment, out of the box integrations, ease of use, and simplicity to help engineers quickly and easily:

- Detect unusual changes instantly: Automatically spot anomalies based on golden signals like throughput, errors, and latency across all applications, services, and log data. Engineers get notified in Slack and other collaboration tools, and can troubleshoot faster with in-depth anomaly analytics to detect potential problems early, before they impact customers.

- Cut down alert noise from any source: Instead of alert storms across multiple tools, events are auto-correlated based on time, context from alert messages, and now relationship data across systems so engineers see one issue with all the data needed to take action. Pre-trained ML models accelerate speed to value by eliminating steep and costly learning curves.

- Get to root cause faster: Eliminate guesswork and solve problems faster with automatic insights into the probable root cause for incidents. Engineers can quickly see why each open issue occurred, which services and systems are impacted, and what action is needed for resolution. They get ML-based guidance on suggested responders on their team who may be best equipped to revolve each issue.

- Detect patterns and outliers in log data: Machine learning detects patterns and outliers in log data to reduce troubleshooting time. Engineers can explore millions of log messages with a single click and reduce manual querying by automatically clustering their log data to quickly find anomalous patterns and problematic needles in the haystack. Because New Relic uniquely enables teams to instrument all telemetry data from any source in one place, log patterns are stored in New Relic's Telemetry Data Platform as events. This enables engineers to easily create dashboards, alerts, and queries based on log patterns for faster rollup analysis and troubleshooting of trends in their log data.

- Integrate seamlessly with PagerDuty and other popular incident management tools: Eliminate the toil of managing incidents across tools via a new integration that synchronizes the state of correlated issues in New Relic bi-directionally with PagerDuty and other popular incident management tools. As the state of correlated issues changes in New Relic and these platforms, they are all now automatically updated to help on-call engineers manage and resolve incidents more efficiently and effectively.

New Relic’s new AIOps capabilities are generally available today to all New Relic Applied Intelligence customers.

Anomaly detection is available now and enabled for all customers at no additional charge, including New Relic free tier users.

Log Patterns is now available in public beta.

The Latest

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 ...