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New Relic Launches Agentic AI Monitoring and MCP Server

New Relic announced two complementary innovations, Agentic AI Monitoring and the New Relic AI Model Context Protocol (MCP) Server, that together transform system complexity into clarity and help businesses deliver more perfect software in the AI age. 

Agentic AI Monitoring provides businesses with holistic visibility into interconnected agents and tools so they can optimize their agentic workforces. The New Relic AI MCP Server opens up a standardized way of enabling a powerful ecosystem of agents that fuel advanced agentic workflows. This integration allows popular AI assistants like GitHub Copilot, ChatGPT, Claude, and Cursor to access detailed New Relic observability data directly, embedding critical insights into engineers' workflows.

“The convergence of AI workloads, cloud-native architectures, and real-time data processing has created a perfect storm of complexity,” said New Relic Chief Product Officer Brian Emerson. “Our platform uses intelligent automation and unified data correlation to diffuse that complexity so you can operate your business confidently and at scale. Our latest innovations further empower enterprises to adopt AI systems that create real business value, rather than cutting into the bottom line.”

New Relic Agentic AI Monitoring provides visibility into every agent and tool call within multi-agent collaborations. Organizations now understand how their mesh of agents communicate with each other, so they can troubleshoot faster and avoid downtime. The capability delivers granular insights into tool utilization, performance, and errors with a view that shows which agents and tools were called — in what order — and key performance data. Users gain a consolidated AI Inventory view of agents and tools as well as detailed insights into their names, latency, and errors. An Agents Service Map visualizes inter-agent interactions, and offers the ability to drill down into individual agent performance details and their traces.

New Relic provides holistic observability across interconnected agents and tools and, critically, also the services and infrastructure they rely on. This enables engineering and DevOps teams to pinpoint issues faster, accelerate root cause analysis, and optimize performance across their entire AI-enabled stack. The capability is only possible by building Agentic AI Monitoring on top of tried-and-true APM and infrastructure monitoring tools like New Relic’s.  

The New Relic AI MCP Server brings New Relic’s capabilities to AI agents, unlocking a crucial ecosystem of integrated tools. Many powerful AI assistants currently operate without key insights into the performance of code running in production. This limits their usefulness and forces developers to toggle between platforms. With the New Relic AI MCP server, engineers can retrieve deep, detailed data and insights from wherever they’re working, making their favorite AI agents instantly more powerful and productive. By eliminating context switching, teams can respond to incidents faster, shorten MTTR, improve uptime, and accelerate time-to-market. Every engineer can now ask questions and receive actionable insights promptly, democratizing observability across the organization.

New Relic is also introducing Outlier Detection, which works in tandem with anomaly detection to detect and analyze aberrant behaviors. New Relic Outlier Detection highlights data points that signal issues or failures, enabling teams to prioritize workstreams and address incidents proactively before they impact end-users. The solution goes beyond industry-standard algorithms to not only flag outliers but also streamline remediation.

Limited previews of Agentic AI Monitoring, the New Relic AI MCP Server, and Outlier Detection are now available as part of the New Relic Intelligent Observability Platform.

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New Relic Launches Agentic AI Monitoring and MCP Server

New Relic announced two complementary innovations, Agentic AI Monitoring and the New Relic AI Model Context Protocol (MCP) Server, that together transform system complexity into clarity and help businesses deliver more perfect software in the AI age. 

Agentic AI Monitoring provides businesses with holistic visibility into interconnected agents and tools so they can optimize their agentic workforces. The New Relic AI MCP Server opens up a standardized way of enabling a powerful ecosystem of agents that fuel advanced agentic workflows. This integration allows popular AI assistants like GitHub Copilot, ChatGPT, Claude, and Cursor to access detailed New Relic observability data directly, embedding critical insights into engineers' workflows.

“The convergence of AI workloads, cloud-native architectures, and real-time data processing has created a perfect storm of complexity,” said New Relic Chief Product Officer Brian Emerson. “Our platform uses intelligent automation and unified data correlation to diffuse that complexity so you can operate your business confidently and at scale. Our latest innovations further empower enterprises to adopt AI systems that create real business value, rather than cutting into the bottom line.”

New Relic Agentic AI Monitoring provides visibility into every agent and tool call within multi-agent collaborations. Organizations now understand how their mesh of agents communicate with each other, so they can troubleshoot faster and avoid downtime. The capability delivers granular insights into tool utilization, performance, and errors with a view that shows which agents and tools were called — in what order — and key performance data. Users gain a consolidated AI Inventory view of agents and tools as well as detailed insights into their names, latency, and errors. An Agents Service Map visualizes inter-agent interactions, and offers the ability to drill down into individual agent performance details and their traces.

New Relic provides holistic observability across interconnected agents and tools and, critically, also the services and infrastructure they rely on. This enables engineering and DevOps teams to pinpoint issues faster, accelerate root cause analysis, and optimize performance across their entire AI-enabled stack. The capability is only possible by building Agentic AI Monitoring on top of tried-and-true APM and infrastructure monitoring tools like New Relic’s.  

The New Relic AI MCP Server brings New Relic’s capabilities to AI agents, unlocking a crucial ecosystem of integrated tools. Many powerful AI assistants currently operate without key insights into the performance of code running in production. This limits their usefulness and forces developers to toggle between platforms. With the New Relic AI MCP server, engineers can retrieve deep, detailed data and insights from wherever they’re working, making their favorite AI agents instantly more powerful and productive. By eliminating context switching, teams can respond to incidents faster, shorten MTTR, improve uptime, and accelerate time-to-market. Every engineer can now ask questions and receive actionable insights promptly, democratizing observability across the organization.

New Relic is also introducing Outlier Detection, which works in tandem with anomaly detection to detect and analyze aberrant behaviors. New Relic Outlier Detection highlights data points that signal issues or failures, enabling teams to prioritize workstreams and address incidents proactively before they impact end-users. The solution goes beyond industry-standard algorithms to not only flag outliers but also streamline remediation.

Limited previews of Agentic AI Monitoring, the New Relic AI MCP Server, and Outlier Detection are now available as part of the New Relic Intelligent Observability Platform.

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Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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 ...