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.
The Latest
Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...
Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...
Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...
Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...
Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...
Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...
If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...
In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...
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 ...