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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

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

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The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

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

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...