March 2026 Summaries

New Relic celebrated Women’s History Month with a unique virtual speed networking event by incorporating the internal theme, "We Give, We Grow," to promote communal support and growth. The initiative included a 2:1 donation match for organizations like Black Girls Code, and featured a panel of women leaders discussing the "Architect of Change" mindset. Workshops on addressing microaggressions and fostering resilience were also held, emphasizing inclusion and well-being. The event was part of New Relic’s global effort to foster a sense of belonging through tailored regional programs, such as leadership panels in the Americas, wellness sessions in APAC, and career storytelling in EMEA. The event highlighted the commitment to a culture where women lead, and the importance of diverse perspectives in driving innovation, with an invitation to explore opportunities within the company.

Modern cloud environments generate extensive telemetry data, leading to challenges in effective monitoring due to noise and duplication. The market for cloud monitoring tools is expected to grow significantly, necessitating careful evaluation of tools based on architecture, data unification, and real-time telemetry correlation. Effective monitoring focuses on explaining the "why" of incidents rather than just the "what," emphasizing the importance of unified telemetry to reduce investigation time. Key players like New Relic, Datadog, Dynatrace, Prometheus, and Amazon CloudWatch offer varied features tailored to different environments, from SaaS platforms to open-source tools. Each platform's ability to ingest, store, correlate, and surface telemetry data is pivotal, as is their integration ecosystem and cost considerations, including total cost of ownership beyond licensing fees. New Relic stands out for its single, engineer-centric platform that unifies metrics, logs, traces, and events, facilitating faster incident response and reduced cognitive load. Choosing the right tool involves assessing current observability gaps, evaluating data unification capabilities, considering total cost of ownership, and testing integration complexity within existing stacks.

AIOps platforms are designed to manage the overwhelming volume of data and alerts generated by modern IT systems, utilizing machine learning to provide actionable insights and reduce noise. Effective AIOps solutions integrate seamlessly with existing observability and incident management tools, enabling faster incident response by correlating metrics, logs, traces, and events into unified views that highlight probable root causes. Among the top AIOps platforms, New Relic stands out for embedding AIOps directly into its observability platform, offering a cohesive solution that reduces context switching and toolchain complexity. Key features to consider when evaluating AIOps tools include intelligent anomaly detection, automated incident correlation, and predictive analytics, all of which contribute to improving operational outcomes like reducing alert fatigue and mean time to resolution (MTTR). The choice of AIOps platform should align with an organization's architecture, data requirements, and workflow preferences, ensuring it enhances rather than complicates existing IT processes.

APM (Application Performance Monitoring) metrics provide critical insights into application performance, infrastructure health, and user experience, enabling software teams to diagnose and resolve issues with data-driven precision. These metrics, which include response time, error rate, CPU usage, and Apdex scores, help teams quickly identify problems, reduce mean time to resolution (MTTR), and optimize resource utilization by offering a unified view that minimizes context switching and cognitive load. By integrating APM metrics into DevOps practices and continuously monitoring them, teams can proactively detect performance issues, enhance user satisfaction, and make informed decisions on resource allocation and system improvements. Tools like New Relic facilitate the collection, analysis, and visualization of these metrics, offering auto-instrumentation and numerous integrations to streamline the implementation process and ensure comprehensive coverage across application performance, infrastructure, and user experience.

New Relic has been recognized as a Leader in the IDC MarketScape: Worldwide AIOps 2026 Vendor Assessment, highlighting its commitment to intelligent observability and AI-driven operational decision-making. The report emphasizes New Relic's strengths, such as outcome-centric decision operations, business journey modeling, predictive capabilities, and open standards leadership, which collectively enable organizations to link technical performance with business outcomes. This recognition underscores New Relic's strategic shift from reactive incident response to proactive, AI-guided workflow integration, aiming to transform complex digital operations into collaborative and outcome-driven processes. The company's platform leverages agentic AI, large language models, and open standards to deliver prescriptive guidance and intelligent workflows, thus bridging the gap between signals and actions in increasingly complex digital environments.

Log management as code is presented as a solution to the challenges of maintaining log configurations manually across numerous services, aiming to establish a self-governing pipeline that standardizes data, ensures performance, secures information by default, and optimizes costs. By using Terraform modules, this approach automates the process, ensuring new services are integrated into a secure and efficient ecosystem without manual intervention. New Relic's Terraform provider is highlighted as a tool to build this self-governing log pipeline, with functionalities like log parsing and enrichment, provisioning log partitions for performance, creating global security modules for obfuscation, and implementing pipeline cloud rules to manage ingest rules efficiently. The text emphasizes the benefits of treating log management as code, encouraging teams to scale their New Relic environments with Terraform and move towards automated, secure observability as part of their CI/CD pipelines.

Modern observability extends beyond merely collecting logs and traces to focusing on actionable signals, with OpenTelemetry (OTel) Events and New Relic Custom Events offering distinct solutions for different observability needs. OpenTelemetry Events are designed to provide engineers with structured, trace-aligned logs that offer detailed diagnostic insights, crucial for debugging and root-cause analysis, without vendor lock-in. Conversely, New Relic Custom Events are geared towards delivering business and AI insights, transforming metrics into dedicated, queryable event types for analytics-ready dashboards, trend analysis, and alerting. As teams navigate increasingly complex architectures and AI-powered services, understanding when to use OTel Events for in-depth diagnostics and when to deploy Custom Events for high-level analytics can transform reactive incident handling into proactive system improvement. This dual-track strategy leverages the strengths of both event types, ensuring a comprehensive observability practice that spans detailed request-level understanding and broader business-level analytics.

Applications and services operate in two states: doing tasks or waiting for events, similar to driving with either movement or pauses. Efficient performance requires minimizing wait times, whether due to direct waits (waiting for other systems) or indirect waits (CPU unavailability due to other processes). Understanding where time is spent can help optimize performance by reducing unnecessary waits and enhancing CPU usage efficiency. Observability tools, such as Application Performance Monitoring (APM) and Distributed Tracing, enable the identification of bottlenecks and offer insights into improving application performance by monitoring interactions between systems and infrastructure. By identifying the reasons behind waits and optimizing code or infrastructure, businesses can improve application performance and reduce costs, as demonstrated by real-world cases where tweaking configurations and optimizing queries led to reduced wait times and enhanced processing efficiency.

In observability workshops, the challenge often shifts from instrumenting systems to managing alert fatigue caused by telemetry, which can hinder effective incident response. To address this, a robust Alert Lifecycle Reference Architecture is suggested, focusing on three domains: Knowledge, Action, and Record. These domains respectively involve detecting and defining alerts, managing notification and response, and maintaining incident history for accountability and governance. The architecture can be implemented using tools like New Relic, which can serve all three roles on a unified data plane, eliminating the need for multi-tool integration and reducing operational complexity. This unified approach allows for seamless incident management, maintaining the integrity and context of data throughout the lifecycle, while clear role definitions and escalation ladders ensure effective human response. The key to success lies in maintaining the architectural discipline, while respecting team boundaries and roles to prevent cultural failures that could undermine the process.

The concept of the Observability Gap highlights the disconnect between infrastructure health indicators and actual service performance, suggesting that traditional monitoring often fails to capture user experience and business outcomes. To address this, a Service Level Management (SLM) Reference Architecture is proposed, which involves defining Service Level Indicators (SLIs) and Service Level Objectives (SLOs) across various system layers, from infrastructure to business metrics. This approach aims to ensure that technical health translates into tangible business outcomes by measuring user experience and system performance at multiple tiers, including the Experience Layer, Gatekeeper Layer, Service Domain, Infrastructure, and Business Outcomes. Each layer involves specific SLIs and SLOs to address unique challenges, such as latency, error rates, cache efficiency, and business metrics like conversion rates and revenue velocity. The architecture underscores the importance of ownership and accountability in SLM, emphasizing that SLOs must be actively managed and connected to business goals to effectively bridge the gap between technical operations and customer satisfaction.

The Extended Berkeley Packet Filter (eBPF) is a transformative technology introduced in Linux 4.x that allows bytecode to run within the Linux kernel, providing secure and efficient access to kernel resources. New Relic has harnessed this technology to launch a new eBPF-based agent that simplifies observability by consolidating network performance, application performance management (APM) telemetry, infrastructure metrics, and logging data into a single stream. This agent, designed to function with minimal overhead, eliminates the need for multiple data collection methods and improves visibility across dynamic environments. By offering insights directly from the kernel, eBPF Network Metrics enhance the ability to diagnose network-related issues, such as TCP handshake latency and DNS failures, without requiring changes to application code. This approach significantly reduces the time required to identify network performance issues, thereby lowering operational costs and improving mean time to resolution. The solution provides a comprehensive view of network behavior, correlates it with application and infrastructure performance, and integrates seamlessly into existing workflows, offering a single-agent, language-agnostic method of collecting kernel-level telemetry.

SAP Managed Service Providers (MSPs) are transitioning from traditional server maintenance roles to becoming orchestrators of business outcomes, driven by the shift towards RISE with SAP and the demand for measurable business results. This evolution requires MSPs to adopt business observability, focusing on process reliability, modernization, and compliance rather than just system uptime. Tools like New Relic facilitate this change by offering end-to-end visibility and real-time insights across SAP and non-SAP systems, enabling faster issue resolution and de-risking SAP migrations. The new approach emphasizes proactive, AI-assisted problem-solving and outcome-based contracts aligned with business KPIs, transforming MSPs from cost centers into strategic business partners.

The New Relic Java Agent is a tool designed to offer comprehensive observability in Java applications with minimal impact on system performance. It operates by using the Java Virtual Machine's (JVM) instrumentation capabilities to monitor code execution, allowing it to handle complex, multi-threaded transactions effectively. The agent can be deployed in two primary modes: a static mode, which is initiated at JVM startup, and a dynamic mode, which can be attached to a running JVM. It uses a "Harvest Cycle" to collect and transmit metrics to New Relic's servers, ensuring efficient memory usage without storing data locally. The agent supports both automatic and custom instrumentation, the latter being useful for tracking proprietary business logic. Additionally, it addresses asynchronous behavior through context propagation and a token-based API, while also employing a circuit breaker to manage system memory effectively. The agent provides actionable insights by transforming telemetry data into a comprehensive view of the system's health, driving operational excellence and system stability.

New Relic is enhancing its observability offerings by integrating with various cloud and AI platforms to provide a unified experience in complex, multi-cloud environments. By partnering with leading service providers like AWS, Azure, and platforms such as ServiceNow and Atlassian, New Relic aims to streamline operations and reduce mean time to resolution by integrating operational contexts seamlessly. The company is also advancing towards intelligent automation, enabling teams to not only identify but autonomously resolve issues, with innovations such as the Model Context Protocol (MCP) Server facilitating real-time data exchange among AI agents. Recent collaborations with GitHub allow for automatic issue detection and resolution drafting using GitHub Copilot, enhancing developer workflows by bringing observability directly into their existing tools. New Relic's Partner Engineering Blog Series highlights these collaborative efforts, which have reportedly increased their customers' ability to rapidly deploy new features and updates. The blog also encourages engagement with the broader community through forums like the Explorers Hub for further discussion and support.

New Relic Control addresses the challenges of observability sprawl in large-scale environments by providing a centralized governance layer for managing agents, fleets, and telemetry pipelines. As organizations expand, traditional observability methods, which rely on local management or static scripts, become inadequate due to fragmentation and rising costs. New Relic Control offers a unified control plane that automates agent management, enforces role-based access control, and facilitates telemetry shaping to optimize costs and data quality. It introduces a federated governance framework, allowing application teams to manage their own instrumentation while maintaining infrastructure stability. This approach not only reduces operational overhead but also enhances innovation by breaking down platform team bottlenecks and ensuring consistent observability standards.