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New Relic Knowledge Introduced

New Relic announced New Relic Knowledge, a new platform capability that integrates telemetry and knowledge sources to enhance detection and resolution of issues in the AI era. 

By fusing real-time telemetry with historical incident data, system changes, and deep operational context, New Relic Knowledge provides the foundational intelligence required for AI agents and engineering teams to understand systems, make decisions, and resolve issues faster. As a result, organizations can mitigate the $76 million risk of median annual downtime by accelerating mean time to resolution (MTTR) to machine speed, turning technical reliability into a measurable business edge.

New Relic Knowledge delivers a continuous intelligence layer that operates across the entire New Relic Intelligent Observability Platform.

“Organizations today must solve technology problems at a pace that far exceeds human scale. While AI agents are addressing this challenge, they are only as effective as the data they can access,” said New Relic Chief Product Officer Brian Emerson. ”New Relic Knowledge provides the connective tissue between telemetry and action, ensuring that every technical decision—whether made by a human or an agent—is grounded in real-world context to drive true business impact.”

In addition to serving AI agents, New Relic Knowledge is purpose-built for SREs, DevOps teams, and platform engineers who are under increasing pressure to maintain uptime in hyper-complex environments. New Relic Knowledge analyzes telemetry across metrics, logs, traces, and events, and correlates it with prior incidents, system changes, and service relationships. It then surfaces relevant context instantly, enabling both engineers and AI agents to move quickly from detection to explanation and resolution.

New Relic Knowledge connects telemetry, documentation, and historical incidents to deliver context aware and trusted insights in real time. Key features and benefits of the capability include:

  • Machine-Speed Troubleshooting: Correlates anomalies with recent deployments and configuration updates instantly, identifying what changed without manual investigation.
  • Agentic Decision Support: Empowers AI agents to diagnose issues and recommend next steps with high confidence by referencing similar past incidents and system behavior patterns.
  • Operational Toil Reduction: Provides context-rich answers embedded directly within existing workflows, such as alert triage and incident response, eliminating the need for engineers to pivot across disparate tools to find answers.
  • Continuous Intelligence: Unlike static knowledge bases, New Relic Knowledge continuously assesses the user's intent and utilizes historical business information to provide responses grounded in proprietary knowledge.

New Relic Knowledge will be generally available to New Relic AI customers on May 25, 2026. 

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 Knowledge Introduced

New Relic announced New Relic Knowledge, a new platform capability that integrates telemetry and knowledge sources to enhance detection and resolution of issues in the AI era. 

By fusing real-time telemetry with historical incident data, system changes, and deep operational context, New Relic Knowledge provides the foundational intelligence required for AI agents and engineering teams to understand systems, make decisions, and resolve issues faster. As a result, organizations can mitigate the $76 million risk of median annual downtime by accelerating mean time to resolution (MTTR) to machine speed, turning technical reliability into a measurable business edge.

New Relic Knowledge delivers a continuous intelligence layer that operates across the entire New Relic Intelligent Observability Platform.

“Organizations today must solve technology problems at a pace that far exceeds human scale. While AI agents are addressing this challenge, they are only as effective as the data they can access,” said New Relic Chief Product Officer Brian Emerson. ”New Relic Knowledge provides the connective tissue between telemetry and action, ensuring that every technical decision—whether made by a human or an agent—is grounded in real-world context to drive true business impact.”

In addition to serving AI agents, New Relic Knowledge is purpose-built for SREs, DevOps teams, and platform engineers who are under increasing pressure to maintain uptime in hyper-complex environments. New Relic Knowledge analyzes telemetry across metrics, logs, traces, and events, and correlates it with prior incidents, system changes, and service relationships. It then surfaces relevant context instantly, enabling both engineers and AI agents to move quickly from detection to explanation and resolution.

New Relic Knowledge connects telemetry, documentation, and historical incidents to deliver context aware and trusted insights in real time. Key features and benefits of the capability include:

  • Machine-Speed Troubleshooting: Correlates anomalies with recent deployments and configuration updates instantly, identifying what changed without manual investigation.
  • Agentic Decision Support: Empowers AI agents to diagnose issues and recommend next steps with high confidence by referencing similar past incidents and system behavior patterns.
  • Operational Toil Reduction: Provides context-rich answers embedded directly within existing workflows, such as alert triage and incident response, eliminating the need for engineers to pivot across disparate tools to find answers.
  • Continuous Intelligence: Unlike static knowledge bases, New Relic Knowledge continuously assesses the user's intent and utilizes historical business information to provide responses grounded in proprietary knowledge.

New Relic Knowledge will be generally available to New Relic AI customers on May 25, 2026. 

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 ...