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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. 

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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

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 ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...