Microsoft Build 2026 kicked off this week in San Francisco with a deluge of announcements spanning artificial intelligence, cloud infrastructure, and next-generation developer tooling. Among the notable voices from the partner ecosystem, New Relic used the spotlight to reinforce a 14-year alliance with Microsoft and to detail a fresh wave of integrations that bring its observability platform deeper into Azure and GitHub Copilot workflows. The company also highlighted double-digit year-over-year growth in its committed book, signaling that organizations are increasingly willing to invest in end-to-end visibility for complex, AI-augmented applications.

A Partnership That Spans the Cloud Era

The relationship between New Relic and Microsoft traces back to the early days of Azure. What began as basic metric and log forwarding from Windows Server has evolved into a strategic, multi-product collaboration. Today, that partnership extends across the entire Microsoft technology stack—from .NET application monitoring to Azure Kubernetes Service insights, and now into the frontier of AI-assisted development and site reliability engineering. At Build 2026, New Relic executives framed the depth of this integration not as a bolt-on, but as a foundational layer for enterprises that run mission-critical workloads on Azure and rely on GitHub for their development lifecycle.

Attendees at the Moscone Center got a firsthand look at how New Relic’s Intelligent Observability Platform is becoming the connective tissue between operational data and developer action. Instead of forcing teams to toggle between Azure Monitor dashboards, GitHub pull requests, and incident response tools, New Relic demonstrated a unified experience that surfaces actionable insights directly where engineers already work.

Azure Agents: Closing the Loop on Incident Response

The centerpiece of the Azure-focused announcements is a new class of what New Relic calls “Azure SRE Agents.” These agents are not simply data collectors; they are intelligent, AI-powered assistants that can automatically diagnose performance issues, correlate anomalies across Azure services, and—in some cases—remediate common problems without human intervention. The agents build on Microsoft’s copilot extensibility framework, allowing them to tap into the same large language models that power Azure Copilot and GitHub Copilot. This means an SRE receiving a late-night alert can query the agent in natural language: “Why did the checkout service latency spike in East US 2?” and receive a detailed root cause analysis generated from telemetry across Azure Front Door, App Service, and Cosmos DB.

Behind the scenes, the Azure SRE Agent is the result of joint engineering work that leverages New Relic’s distributed tracing and anomaly detection with Azure’s vast operational surface area. The agents can be deployed as lightweight sidecar containers within Azure Container Apps or as extensions in Azure Functions, which drastically lowers adoption friction. New Relic reports that early access customers have reduced mean time to resolution (MTTR) by up to 40% for common cloud-native issues.

Crucially, the agents respect the security boundaries of Azure’s resource manager. They operate with just-in-time access credentials, and all remediation recommendations—and any automated actions—are fully audited within Azure Policy and Microsoft Sentinel. For regulated industries, this means that the long-held tension between speed of recovery and compliance rigor is now easier to navigate.

AI Observability Arrives for GitHub Copilot

Perhaps the most forward-looking demonstration at Build 2026 centered on AI observability for code generated by GitHub Copilot. As organizations accelerate their adoption of AI-powered coding assistants, a new challenge has emerged: how do you monitor the performance, cost, and reliability of code that a developer may not have fully written themselves? New Relic’s answer is a dedicated Copilot observability module that plugs directly into the GitHub ecosystem.

The module works by instrumenting the output of Copilot suggestions at the point of commit. When a developer accepts a Copilot-generated function, New Relic’s agent—already embedded in the application runtime—can tag that code block with metadata that traces its origin. This allows platform teams to build dashboards that compare the error rates, latency, and resource consumption of hand-written code versus AI-generated code. Over time, the system can even flag patterns: for instance, if Copilot-suggested database queries consistently introduce N+1 problems, the observability layer surfaces that trend so engineering managers can adjust coding guidelines or fine-tune the AI model prompts.

During a live demo on the Build stage, a New Relic engineer showed how a single Copilot-generated method, once deployed to production in an Azure Container App, triggered an alert because it repeatedly called an unindexed Cosmos DB property. The alert included a one-click link to the exact pull request and the Copilot interaction history, enabling the developer to understand both the technical and the contextual root cause. The crowd’s reaction—a mix of applause and surprised murmurs—suggested that the implications are only beginning to be grasped.

For enterprise customers, this capability answers a growing need for governance over AI-generated software. Where earlier generations of observability focused on “who wrote the code” via git blame, the next generation must also track “what wrote the code.” New Relic’s integration with GitHub Copilot makes the source of intelligence a first-class dimension in every query, next to host ID, region, and transaction name.

Observability in the Flow of Work

Beyond the headlining AI features, New Relic also announced deeper integration with Azure DevOps and GitHub Actions. Now, when a deployment pipeline fails due to a performance regression caught by New Relic’s Synthetics checks, the CI/CD runner can inject a detailed report directly into the pull request comments. Developers no longer need to leave GitHub to understand why a deployment was gated; the observability verdict is right there in the conversation thread.

This philosophy of “observability in the flow of work” extends to Microsoft Teams and Outlook as well. New Relic showed new adaptive cards that deliver context-rich alerts into the collaboration surface where incident responders are already coordinating. A critical spike in Azure SQL CPU can now present as a Teams message with a graph of the anomaly, the top offending queries, and buttons to roll back a recent migration or scale the database tier—all without opening a separate console.

These integrations reflect a broader industry shift. The observability market has matured beyond the “single pane of glass” ideal into an era where telemetry data must be woven into the fabric of the developer platform. New Relic’s bet is that its platform can serve as the open, unified data layer across the Microsoft ecosystem, while Microsoft’s steady expansion of AI co-pilots creates more and more signals that need to be observed.

Double-Digit Growth Signals Market Validation

Amid the product announcements, New Relic’s leadership shared a notable business metric: double-digit year-over-year growth in its committed book, with a particularly strong contribution from Azure-native customers. While the company did not break out specific dollar figures, the momentum underscores a broader trend in enterprise IT spending. Companies that have gone all-in on Azure are now maturing their operations and are willing to pay for premium observability that transcends what native tools can offer.

Analysts in attendance noted that observability spending continues to grow faster than general IT budgets, driven by the complexity of microservices and AI workloads. “You can’t optimize what you can’t see,” was a refrain heard throughout the Build conference, and New Relic’s double-digit growth supports the idea that seeing deeply is now a boardroom priority. The company hinted that its consumption-based pricing model, combined with Azure Marketplace availability, lowers the procurement barrier and allows teams to start small before expanding to full-stack monitoring.

That growth also reflects the symbiotic nature of the Microsoft partnership. As Azure’s own feature velocity increases—with new services like Azure AI Studio and Fabric transforming the data landscape—the surface area of possible failure modes expands. New Relic’s role is to provide a consistent observability plane that keeps pace with that innovation. The 14-year history means that when a brand-new Azure service launches, New Relic often has instrumentation ready on day one, giving its customers the confidence to adopt bleeding-edge capabilities without sacrificing reliability.

The Road Ahead: From Monitoring to Intelligence

Looking forward, both New Relic and Microsoft signaled that their collaboration will only deepen as AI becomes the primary interface for both building and operating software. Concepts like the Azure SRE Agent and AI observability for Copilot are not one-off features; they are part of a vision where observability platforms become true operational partners. In this vision, systems will self-diagnose, developers will receive proactive suggestions before they even write a buggy line of code, and site reliability engineers will spend less time on triage and more time on architecture.

The announcements at Build 2026 set the stage for what comes next: tighter Kubernetes native integrations, observability for large language model fine-tuning processes, and dashboards that automatically reconfigure based on what an AI planner determines is most relevant to an ongoing incident. New Relic’s commitment to open standards like OpenTelemetry ensures that none of this creates new lock-in; instead, it encourages a rich ecosystem where data flows freely between tools.

For the thousands of developers and IT leaders who flocked to San Francisco this week, the message was clear. The partnership between New Relic and Microsoft is no longer just about keeping the lights on for Azure VMs. It is about enabling an intelligent, self-healing cloud that understands not just what is happening, but why—and can act on that understanding in seconds. As the Moscone Center halls emptied and attendees headed to the evening receptions, the conversations centered not on if AI would change operations, but how quickly.