April 2026 Summaries

New Relic has expanded its eBPF-based monitoring capabilities to provide full-stack observability across Kubernetes, hosts, and networks, allowing platform engineers, SREs, and DevOps teams to correlate application performance with underlying system and network behavior. This evolution from its original eAPM for Kubernetes simplifies root cause analysis by offering real-time insights into TCP and DNS activity at the kernel level without requiring code changes or manual instrumentation. By integrating into the New Relic Intelligent Observability Platform, users can leverage AI-powered insights and OpenTelemetry-based instrumentation to improve cost efficiency and reduce operational complexity. This comprehensive approach enables teams to swiftly identify performance bottlenecks across distributed systems, enhancing their ability to manage modern environments effectively.

At the NAB Show 2026 in Las Vegas, discussions centered around the growing presence of AI in the media and entertainment industry, highlighting a gap in conversations about operationalizing AI rather than just building it. The event saw a significant increase in AI exhibitors, with a focus on content creation and personalization, yet few addressed the challenges of running AI workloads effectively. Stephen Shaw from New Relic emphasized the need for intelligent observability in AI systems, advocating for a shift from fragmented point solutions to unified platforms that provide comprehensive visibility across the media stack. This approach is crucial for addressing persistent industry challenges such as quality of experience and operational efficiency, which are exacerbated by AI's complexity. Shaw's session underscored the importance of monitoring AI workloads to prevent costly outages and enhance streaming performance, with case studies demonstrating substantial improvements in media operations through integrated AI monitoring solutions.

New Relic undertook a significant migration from Snowflake to an open, cloud-native architecture using Apache Iceberg, Amazon S3, Apache Spark, and other open-source technologies to address escalating costs and vendor lock-in issues. By decoupling storage, compute, and query layers, the company achieved a 35-52% reduction in data platform costs while ensuring scalable and efficient operations. The migration involved running both systems in parallel, ensuring row-level data parity before transitioning consumers, and utilizing full-stack observability tools to maintain visibility and reliability throughout the process. The shift allowed New Relic to operate a more flexible and cost-effective data platform that can adapt to future technological advancements without being tied to a single vendor.

Modern operations teams often face alert fatigue, where monitoring systems indicate issues, but human intervention is required to diagnose and fix them, leading to delays and potential financial and reputational damage. New Relic's Workflow Automation addresses this challenge by introducing a low-code orchestration layer that transforms observability data into automated, actionable responses. By connecting data from New Relic to cloud-native platforms and other API-driven systems, Workflow Automation enables automatic remediation actions, such as rolling back deployments or scaling virtual machines, without manual intervention. It incorporates control logic, human oversight for critical actions, and a comprehensive execution model to ensure reliable, maintainable automation. The system supports building workflows through a visual interface or YAML scripting, allowing teams to automate incident response and infrastructure management efficiently. By codifying incident response procedures, Workflow Automation reduces mean time to resolution (MTTR), minimizes human error, and allows engineers to focus on complex issues, ultimately transforming observability from a passive monitoring tool into an active remediation system.

The text discusses the importance of effective Kubernetes application monitoring in minimizing downtime and optimizing resource use by correlating application behavior with cluster state, logs, and traces. It emphasizes choosing the right tools that unify metrics, traces, logs, and Kubernetes metadata to enhance incident resolution and reduce mean time to resolution (MTTR). The text reviews top Kubernetes monitoring tools, including New Relic, Datadog, Prometheus + Grafana, Dynatrace, and Elastic Observability, each with their unique strengths, such as automation, AI-assisted analysis, and integration capabilities. It also outlines a practical implementation roadmap for Kubernetes monitoring, covering assessing current environments, choosing instrumentation, deploying agents, and configuring data collection for unified visibility. The goal is to achieve full-stack visibility that not only reduces alert fatigue but also enhances the team's ability to ship features and protect customer experience.

New Relic has introduced a mobile session replay feature to enhance mobile app troubleshooting by offering a visual replay of user interactions, such as taps and swipes, which are synchronized with logs and telemetry data for efficient root-cause analysis. This feature, designed for seamless onboarding through the New Relic platform, allows developers to gain a deeper understanding of app performance by visualizing the user journey, thereby enabling better customer experiences and revenue generation. Privacy is prioritized with stringent masking controls for Personally Identifiable Information, ensuring sensitive data remains secure on the user’s device. The session replay feature is adaptable with customizable sampling rates and privacy settings, and users are billed based on data ingestion rather than individual sessions, offering cost-effective monitoring. This tool marks a significant advancement in digital experience monitoring by providing developers with unprecedented insights into the real-time experiences of their mobile app users.

Effective infrastructure monitoring involves focusing on critical metrics that reveal user impact, service health, and operational risks rather than tracking every available metric. By understanding the roles of metrics, KPIs, SLIs, and SLOs, teams can transition from detection to diagnosis and prioritization, ultimately transforming raw telemetry into actionable insights that connect to user experience, reliability goals, and business outcomes. New Relic facilitates this process by unifying telemetry, allowing teams to correlate signals across multiple layers and streamline troubleshooting with AI-assisted analysis. The guide emphasizes prioritizing user impact, establishing baselines, and designing actionable alerts to maintain clarity and reduce noise, advocating for a structured approach to infrastructure metrics that aligns with business objectives and supports proactive system optimization.

New Relic Cloud Cost Intelligence, generally available from April 28, 2026, aims to address the growing complexity of cloud costs by providing real-time visibility and decision support within New Relic’s Intelligent Observability platform. This tool is designed to help engineering, finance, and product teams align cloud spending with business priorities, thereby reducing overspend, improving accountability, and supporting efficient cloud management. It transforms cloud cost management from a siloed financial task to a collaborative, cross-functional effort, enabling teams to monitor, analyze, and optimize cloud and Kubernetes costs directly alongside performance and reliability data. By delivering telemetry-backed recommendations and real-time updates, Cloud Cost Intelligence helps organizations make informed decisions about cloud investments and resource allocation, thereby enhancing efficiency and effectiveness.

New Relic Cloud Cost Intelligence, now generally available as of April 28, 2026, aims to address the growing complexity in cloud cost management by offering real-time visibility and decision support for engineering, finance, and product teams. Built into New Relic’s Intelligent Observability platform, it allows organizations to monitor, analyze, and optimize cloud and Kubernetes costs within their existing workflows, providing insights into cost drivers, accountability, and efficiency improvements. Cloud Cost Intelligence helps connect cloud spending with business priorities, enabling teams to act on cost insights quickly through features like NRQL, dashboards, and telemetry-backed recommendations. The solution is designed to overcome the limitations of traditional cost-management tools by integrating cost data with operational context, thus helping organizations reduce waste and improve budget management. As the cloud cost management market grows, the tool offers a unified approach to understanding cloud investments, ensuring they align with business outcomes and priorities.

New Relic's internal engineering and data science team has transitioned from manual debugging to using New Relic AI Monitoring (AIM) for intelligent observability of AI agents, significantly enhancing their ability to scale and optimize agent performance and costs. This shift allowed the team to automate telemetry tracking, streamline debugging, and optimize costs through continuous observability across staging and production environments. By leveraging AIM, the team improved their workflows, managed AI agent performance, and ensured cost-effective solutions by monitoring token usage and response times, thus enabling them to focus on developing the next generation of AI agents. The adoption of AIM also facilitated faster development processes with automated tracking and out-of-the-box metrics, allowing the team to move away from building custom tools and concentrate on innovation.

At AWS Summit London, New Relic was recognized as one of the few partners to surpass $1 billion in transactions through AWS Marketplace, highlighting a significant collaboration between New Relic and Amazon Web Services (AWS) over several years. This milestone underscores the joint efforts of both teams to integrate New Relic's observability tools into AWS infrastructure, providing comprehensive solutions for customers across various industries such as finance, insurance, retail, and enterprise ERP. The partnership has evolved to include deep integrations, such as the AWS DevOps Agent and Workflow Automation, which enhance incident response and operational efficiency. This collaboration has been pivotal in helping businesses leverage AI-driven insights and agentic workflows, reflecting New Relic's commitment to delivering intelligent observability that supports modern cloud operations. New Relic's continued focus on innovation and strategic partnerships is expected to drive further advancements in observability solutions, promising more developments in the future.

At the IDC Directions 2026 conference in Boston, the focus was on transitioning from AI experimentation to enterprise-wide implementation, emphasizing the rise of the "Agent Economy" where AI agents are projected to exceed one billion by 2029. Observability is becoming crucial for managing these agents, requiring advancements beyond traditional methods to include agentic observability that ensures reliability, expected behavior, and positive business outcomes. Key to success are four pillars: Knowledge Fabric, Agent Orchestration, Decision Intelligence, and Agentic AI Security, all of which necessitate a modern observability platform capable of deep contextual analysis and intelligent supervision. The AI infrastructure is expected to see over $1 trillion in investments by 2029, necessitating comprehensive observability to manage complexity and performance. Governance and risk management are essential as AI becomes integral, with new models like IDC Quanta and collaborations enhancing AI-powered intelligence layers. At New Relic, initiatives align with these trends, focusing on open agentic communication and intelligent observability, which positions them as a leader in AIOps according to IDC's recent assessment.

Modern distributed systems produce a vast array of telemetry data, which can be overwhelming during network incidents if not properly contextualized. Effective network monitoring tools should not only collect data but also provide context by correlating network behaviors with applications, services, and recent changes. This helps teams make quick, informed decisions and detect issues before they impact users. The most beneficial features include near-real-time data, intelligent alerting, cross-telemetry correlation, and integration with existing systems. Monitoring tools must adapt to complex, distributed environments and support various architectures like cloud-native and traditional infrastructures. Choosing between open-source and commercial tools involves considering the balance between flexibility and operational overhead. Ultimately, the goal is unified observability, which integrates network telemetry with full-stack context, enabling teams to diagnose incidents swiftly and reduce downtime.

Modern network monitoring requires more than accumulating vast amounts of data; it demands clarity, context, and swift action to maintain system reliability. The focus should be on understanding the impact and addressing real incidents quickly, rather than being overwhelmed by metrics noise. Effective monitoring practices encourage integrating network data with application and infrastructure telemetry to resolve issues collaboratively, rather than attributing blame to isolated components. Using unified observability platforms like New Relic can streamline this process by providing a comprehensive view that ties together metrics, logs, traces, and network telemetry. This approach facilitates faster decision-making during incidents and helps prevent recurring issues, ultimately enhancing the user experience. A practical strategy involves concentrating on high-impact services, reducing tool sprawl, and standardizing alerts and responses across teams to ensure that network monitoring is actionable and aligned with engineer workflows.

In cloud environments, effective monitoring requires more than just collecting data and creating dashboards; it demands unifying telemetry, reducing noise, and ensuring engineers have the context needed for quick, informed decision-making. Key best practices include designing monitoring around engineer workflows rather than static dashboards, prioritizing actionable signals linked to user impact and business outcomes, and correlating metrics, logs, traces, and events to facilitate end-to-end problem-solving. Dynamic, context-aware alerting helps reduce noise, while standardizing telemetry enables better cross-team collaboration. A unified observability platform like New Relic can integrate diverse telemetry types into one data platform, aiding in effective troubleshooting and incident resolution by providing a comprehensive view of complex interactions. By focusing on these best practices, teams can avoid common pitfalls, such as fragmented monitoring and engineer burnout, ultimately leading to faster, data-driven decisions and improved system reliability.

By 2026, the integration of Artificial Intelligence (AI) into enterprise operations has evolved from exploratory phases to becoming a core element of business strategy, leading to what is described as the "AI Gold Rush." This movement is characterized by the widespread deployment of autonomous digital systems capable of handling complex tasks, from supply chain management to customer financial advice. Despite significant investments, businesses face challenges with AI's unpredictability and the so-called "AI productivity paradox," where the technology's potential is hindered by issues like probabilistic errors and digital noise. New Relic's AI Monitoring emerges as a solution, transforming AI's opaque nature into a transparent, manageable system that aligns AI initiatives with business objectives through real-time tracking, cost management, and observability. By focusing on critical metrics such as cost per successful resolution and ensuring data-driven performance, New Relic aims to bridge the gap between AI potential and tangible business outcomes. As the landscape becomes increasingly complex, with predictions of over a billion AI agents by 2029, the emphasis shifts from mere monitoring to orchestrating AI operations, enabling organizations to maintain trust, efficiency, and innovation in a rapidly advancing technological environment.

New Relic has achieved a perfect score of 100 on the Human Rights Campaign Foundation's 2026 Corporate Equality Index, recognizing its efforts in promoting LGBTQ+ workplace inclusion. This accolade highlights New Relic's commitment to fostering an inclusive environment, as celebrated by Thomas Lloyd, the Chief Business and Operations Officer, who credits the work of their Employee Resource Groups (ERGs) and peer mentoring initiatives. The company has also launched the New Relic Inclusion, Social Interactions and Engagement (NRISE) Conference, aimed at professional development and reinforcing a sense of belonging among employees. While proud of these achievements, New Relic acknowledges the ongoing journey towards a more inclusive workplace, emphasizing the collective effort required to maintain and enhance this environment.