New Relic Unveils Agentic AI Monitoring and MCP Server to Boost Enterprise AI Adoption

New Relic Unveils Agentic AI Monitoring and MCP Server to Boost Enterprise AI Adoption

New Relic Launches Agentic AI Monitoring and MCP Server to Accelerate AI Adoption and Observability Workflows in the Enterprise

San Francisco, CA , 07th November, 2025: New Relic today announced two major innovations Agentic AI Monitoring and the New Relic AI MCP Server  designed to help enterprises accelerate AI adoption and bring observability into every layer of their AI-driven operations. These capabilities empower organizations to monitor complex AI systems, optimize performance, and deliver measurable business value with confidence.

“The convergence of AI workloads, cloud-native architectures, and real-time data processing has created a perfect storm of complexity,” said Brian Emerson, Chief Product Officer, New Relic. “Our platform uses intelligent automation and unified data correlation to diffuse that complexity so businesses can operate confidently at scale. These innovations empower enterprises to adopt AI systems that deliver real value  not costs.”

According to the 2025 Observability Forecast, AI monitoring adoption has risen from 42% in 2024 to 54% in 2025, as enterprises grapple with rising downtime costs now averaging $2 million per hour. Without modern observability built for AI, silent issues in large language model (LLM) and agentic AI systems can cascade through workflows unnoticed.

Observability for Agentic AI

As organizations deploy sophisticated agentic AI systems, complexity scales rapidly. Agents often depend on each other’s outputs, shared memory, and multiple MCP servers, making debugging increasingly difficult. A single hallucination can cascade across interconnected agents, obscuring the root cause.

New Relic Agentic AI Monitoring delivers full visibility into these intricate multi-agent interactions. The solution provides granular insights into tool usage, performance, and error rates, allowing teams to pinpoint issues faster and reduce downtime. An Agents Service Map visualizes inter-agent dependencies and workflows, while a unified AI Inventory View consolidates data on agent names, latency, and errors.

Unlike traditional LLM monitoring solutions, New Relic’s approach provides end-to-end observability across agents, tools, services, and infrastructure. Built on New Relic’s proven APM and infrastructure monitoring foundation, this capability enables DevOps teams to accelerate root cause analysis, optimize performance, and maintain resilience across their AI stack.

Introducing the New Relic AI MCP Server

The New Relic AI MCP Server extends observability directly into AI agents, making telemetry data instantly accessible within MCP-compatible workflows. By integrating New Relic data with AI assistants, engineers gain real-time performance insights without switching platforms — enhancing productivity, uptime, and response times.

“Enterprises deploying agentic AI to accelerate software delivery have lacked direct access to observability data within their workflows,” said Stephen Elliot, Group Vice President at IDC. “By integrating observability capabilities with MCP-compatible agents, platforms like New Relic create an intelligent feedback loop making AI systems more reliable while observability itself becomes more proactive. This builds the confidence enterprises need to innovate at speed.”

Enhanced Detection with Outlier Analysis

Alongside these launches, New Relic introduced Outlier Detection, which complements anomaly detection by identifying and analyzing unusual patterns that could signal issues or system failures. The tool not only flags outliers but also prioritizes them for proactive remediation enabling teams to address incidents before they impact end users.