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New Relic Introduces Intelligent Workloads

New Relic announced a series of platform innovations that connect technical performance to customer impact and business outcomes. 

Led by Intelligent Workloads that automate the discovery of complex dependencies and align system health with business KPIs, New Relic monitors what matters to its customers in the AI era, empowering them to resolve incidents faster and quantify the direct impact of performance on revenue.

New Relic augments traditional APM metrics from all of an enterprise’s apps — including those built via AI — with business understanding by delivering a complete view of a customer’s digital journey and the organization’s third-party data.  

“Companies that will thrive in the AI era understand that observability is no longer solely focused on systems performance. It’s now part of the business conversation,” said New Relic Chief Product Officer Brian Emerson. “For organizations, the risk is no longer just outages, it’s being blind to business impact. If teams can’t quickly and confidently answer what changed, how their customers were impacted, or how revenue was impaired, decisions slow down while risk quietly compounds. Drawing on our roots in APM, we’ve evolved monitoring to stay ahead of customer needs and directly align with business metrics.”  

New Relic’s Intelligent Workloads automate the discovery and mapping of complex dependencies for a 360-degree view of performance, infrastructure, user impact and business outcomes. Leaders can move beyond "green or red" technical indicators and instead quantify exactly how service performance impacts KPIs like revenue, abandoned carts, and user experience. This context-aware observability enables teams to resolve incidents faster, manage modern transaction-oriented and agentic AI workloads with precision, and rapidly quantify the business impact of technical issues—transforming observability from a maintenance task into a strategic driver of service reliability and business growth.

New Relic has enhanced its Digital Experience Monitoring (DEM) solution to meet the monitoring needs of customers with micro front-end (MFE) architecture, where Web apps are broken down into smaller components and often managed by multiple developer teams. Customers can now monitor every component of their MFE architecture and collect metrics on performance timing, errors, renders and lifecycle methods in order to understand upstream/downstream relationships, effects and dependencies that impact the digital customer experience.

New Relic’s new capabilities for Agentic AI Monitoring include a service map of all interactions between agents, a concise view of agent performance (e.g., number of requests, average latency and error percentages) and a drill down into the trace of a called agent/tool, accelerating problem resolution and improving overall operational efficiency.

New Relic introduced several new intelligent platform capabilities:

  • New Relic Lens: To understand the business impact of application performance issues, errors or the costs associated with a product or feature, one needs to combine observability data with data that lives in other systems such as Postgres, Snowflake, or in-house SQL databases. New Relic Lens allows users to connect and query multiple external data sources, all from within the New Relic UI. Users can combine, analyze and correlate telemetry and non-telemetry (e.g., business) data using sophisticated cross-database joins without ingesting the external data.
  • Federated Logs: Enables teams to query data directly at its source, extracting full value and in-context insights without the need for custom schemas or re-ingestion. Using the Pipeline Control Gateway (PCG), logs stored in Amazon S3 storage are automatically processed and formatted, keeping raw data securely within local customer environments so engineers can access granular insights within the same UI, seamlessly integrated with the rest of the stack. This solution accelerates troubleshooting by eliminating manual toil and context switching, ensuring teams meet data residency mandates while maintaining 100% visibility for critical issue resolution.
  • eBPF: Network Metrics: Provide lightweight kernel-level network visibility that complements APM, with zero instrumentation across application, infrastructure, and network layers. The granular Transmission Control Protocol (TCP) and Domain Name System (DNS) metrics, including handshake latency and DNS failures, address blind spots in troubleshooting. Process-level attribution ties network issues back to the originating process, speeding up root cause analysis.
  • New Relic Notebooks: Designed to transform one-off queries into dynamic runbooks, New Relic Notebooks allow users to leverage variables to create adaptable, repeatable investigative flows that can be linked directly to alerts. By combining queries and visualizations with descriptions, teams can document every step of their process. As a saved document, New Relic Notebooks enable users to store their analytical workflows and easily share them with colleagues, making them an essential tool for collaborative knowledge transfer and post-mortem reviews.
  • Homepage: Once the responding engineer has added data to the New Relic Notebook, they return to their Homepage — a personalized start page designed to eliminate context-switching. The intelligent Homepage provides a customizable workspace and an intelligent launchpad for accessing the platform capabilities, entities, dashboards and favorite items essential to daily work.

Intelligent Workloads are available as a preview for Transaction 360 users as part of the New Relic Intelligent Observability Platform. The other innovations are available as a preview to all users. 

The Latest

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 ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

New Relic Introduces Intelligent Workloads

New Relic announced a series of platform innovations that connect technical performance to customer impact and business outcomes. 

Led by Intelligent Workloads that automate the discovery of complex dependencies and align system health with business KPIs, New Relic monitors what matters to its customers in the AI era, empowering them to resolve incidents faster and quantify the direct impact of performance on revenue.

New Relic augments traditional APM metrics from all of an enterprise’s apps — including those built via AI — with business understanding by delivering a complete view of a customer’s digital journey and the organization’s third-party data.  

“Companies that will thrive in the AI era understand that observability is no longer solely focused on systems performance. It’s now part of the business conversation,” said New Relic Chief Product Officer Brian Emerson. “For organizations, the risk is no longer just outages, it’s being blind to business impact. If teams can’t quickly and confidently answer what changed, how their customers were impacted, or how revenue was impaired, decisions slow down while risk quietly compounds. Drawing on our roots in APM, we’ve evolved monitoring to stay ahead of customer needs and directly align with business metrics.”  

New Relic’s Intelligent Workloads automate the discovery and mapping of complex dependencies for a 360-degree view of performance, infrastructure, user impact and business outcomes. Leaders can move beyond "green or red" technical indicators and instead quantify exactly how service performance impacts KPIs like revenue, abandoned carts, and user experience. This context-aware observability enables teams to resolve incidents faster, manage modern transaction-oriented and agentic AI workloads with precision, and rapidly quantify the business impact of technical issues—transforming observability from a maintenance task into a strategic driver of service reliability and business growth.

New Relic has enhanced its Digital Experience Monitoring (DEM) solution to meet the monitoring needs of customers with micro front-end (MFE) architecture, where Web apps are broken down into smaller components and often managed by multiple developer teams. Customers can now monitor every component of their MFE architecture and collect metrics on performance timing, errors, renders and lifecycle methods in order to understand upstream/downstream relationships, effects and dependencies that impact the digital customer experience.

New Relic’s new capabilities for Agentic AI Monitoring include a service map of all interactions between agents, a concise view of agent performance (e.g., number of requests, average latency and error percentages) and a drill down into the trace of a called agent/tool, accelerating problem resolution and improving overall operational efficiency.

New Relic introduced several new intelligent platform capabilities:

  • New Relic Lens: To understand the business impact of application performance issues, errors or the costs associated with a product or feature, one needs to combine observability data with data that lives in other systems such as Postgres, Snowflake, or in-house SQL databases. New Relic Lens allows users to connect and query multiple external data sources, all from within the New Relic UI. Users can combine, analyze and correlate telemetry and non-telemetry (e.g., business) data using sophisticated cross-database joins without ingesting the external data.
  • Federated Logs: Enables teams to query data directly at its source, extracting full value and in-context insights without the need for custom schemas or re-ingestion. Using the Pipeline Control Gateway (PCG), logs stored in Amazon S3 storage are automatically processed and formatted, keeping raw data securely within local customer environments so engineers can access granular insights within the same UI, seamlessly integrated with the rest of the stack. This solution accelerates troubleshooting by eliminating manual toil and context switching, ensuring teams meet data residency mandates while maintaining 100% visibility for critical issue resolution.
  • eBPF: Network Metrics: Provide lightweight kernel-level network visibility that complements APM, with zero instrumentation across application, infrastructure, and network layers. The granular Transmission Control Protocol (TCP) and Domain Name System (DNS) metrics, including handshake latency and DNS failures, address blind spots in troubleshooting. Process-level attribution ties network issues back to the originating process, speeding up root cause analysis.
  • New Relic Notebooks: Designed to transform one-off queries into dynamic runbooks, New Relic Notebooks allow users to leverage variables to create adaptable, repeatable investigative flows that can be linked directly to alerts. By combining queries and visualizations with descriptions, teams can document every step of their process. As a saved document, New Relic Notebooks enable users to store their analytical workflows and easily share them with colleagues, making them an essential tool for collaborative knowledge transfer and post-mortem reviews.
  • Homepage: Once the responding engineer has added data to the New Relic Notebook, they return to their Homepage — a personalized start page designed to eliminate context-switching. The intelligent Homepage provides a customizable workspace and an intelligent launchpad for accessing the platform capabilities, entities, dashboards and favorite items essential to daily work.

Intelligent Workloads are available as a preview for Transaction 360 users as part of the New Relic Intelligent Observability Platform. The other innovations are available as a preview to all users. 

The Latest

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 ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.