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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

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

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

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...