July 2026 Summaries

New Relic has introduced the Public Preview of its eBPF Logs, enabling automatic collection and correlation of application logs with services, without requiring additional agents or changes to application code. This development builds on New Relic's existing eBPF-based solutions, which already provide application monitoring and network visibility through a single deployment. By incorporating logs into this unified workflow, organizations can achieve a more streamlined and secure observability process, reducing the need for multiple agents and minimizing the operational attack surface. The eBPF technology, a secure and sandboxed Linux platform, facilitates this integration by allowing New Relic to observe system behavior without altering application code, thus offering a seamless experience for developers. This innovation allows teams to quickly access contextual log data, simplifying troubleshooting and decision-making during incidents, exemplified by a scenario where an FX service's log analysis leads to a rapid resolution of price quote rejections. As part of its OpenTelemetry-first approach, New Relic ensures that eBPF Logs fit into open, standards-based pipelines, maintaining user control over data and enabling filtering of sensitive information.

The New Relic Salesforce Exporter is a tool that centralizes Salesforce telemetry, including usage, performance, errors, and security metrics, into the New Relic platform, enabling proactive optimization and observability. By offering real-time monitoring and alerting on system performance, user interactions, security compliance, and organizational limits, it helps organizations transition from reactive troubleshooting to proactive management. The solution supports its observability strategy with two open-source integrations: Event Log Integration for collecting historical data and Event Stream Integration for real-time event visibility. To facilitate quick deployment, New Relic provides an Installer Tool that simplifies the configuration process, allowing users to set up their Salesforce observability environment quickly. The system also includes out-of-the-box dashboards for effective data visualization, and the blog post emphasizes the personal journey and expertise of Andreu Santarén, a software engineer at New Relic, who advocates for using the platform to capture critical business telemetry.

Since its open-source release by Anthropic in late 2024, the Model Context Protocol (MCP) has rapidly become the leading protocol for connecting AI agents to external tools, APIs, and data sources, gaining support from over 150 organizations and integration across major cloud platforms. The latest revision introduces a significant shift to a stateless model, simplifying the protocol by eliminating the need for session management, thereby reducing infrastructure complexity and enhancing scalability. This update allows any server instance to handle any request by embedding context within each request, which facilitates global deployment and improves operational efficiency. The new specification also integrates OpenTelemetry, replacing the proprietary logging channel to enhance observability by enabling comprehensive monitoring and debugging of agent interactions across technology stacks. These changes aim to streamline AI integrations, offering better scalability, flexibility, and visibility into AI agent operations.

Amazon CloudWatch is a native AWS monitoring service that provides basic resource health insights within a single-cloud AWS environment, but it can encounter limitations as infrastructures grow more complex and multi-cloud environments are adopted. While CloudWatch is effective for AWS-only setups, its fragmented nature can lead to increased costs and operational inefficiencies, especially when dealing with multi-cloud architectures. Alternatives to CloudWatch, like New Relic, Datadog, Dynatrace, Grafana Cloud, and Splunk Observability, offer unified observability platforms that deliver multi-cloud visibility, consolidate telemetry data, and provide AI-powered insights for faster incident resolution. These platforms are designed to integrate with AWS and other cloud services, enabling seamless data correlation across distributed systems and offering more predictable pricing models. Many teams opt to use CloudWatch alongside other observability tools during migration to maintain AWS-native visibility while benefiting from broader infrastructure monitoring and more efficient troubleshooting workflows.

AppDynamics, a leading application performance management (APM) platform, is being challenged by the need for broader full-stack observability in modern distributed systems, prompting organizations to explore alternatives. The observability platform market is expected to grow significantly, driven by a demand for unified visibility across complex systems. Many enterprises are moving away from AppDynamics due to its per-agent pricing model, which can lead to unpredictable costs as cloud-native infrastructures scale. Alternatives like New Relic, Dynatrace, Datadog, Splunk APM, SolarWinds SAM, and SigNoz offer varied approaches to observability, focusing on unified telemetry, AI-assisted diagnostics, and flexible pricing models. New Relic's platform, for example, consolidates metrics, logs, traces, and infrastructure telemetry into a single interface, offering a consumption-based pricing model that allows for cost predictability. Organizations are encouraged to evaluate observability platforms based on their ability to support distributed systems and scalable monitoring workflows, with a phased migration strategy recommended to minimize risks associated with switching platforms.

AI agents are increasingly taking over tasks traditionally handled by search engines, necessitating robust observability to ensure reliability as they shift to multi-agent architectures. This blog discusses the critical role of OpenTelemetry traces in monitoring AI agents, using a travel-planning application as a case study, where a multi-agent app orchestrates various specialized agents to create a travel itinerary. The importance of telemetry data, especially traces, is emphasized in diagnosing issues like hallucinations and performance bottlenecks, which are common when AI agents operate autonomously. In environments like Amazon Bedrock AgentCore, where direct access to logs is restricted, these traces become vital for understanding execution flow and debugging. The article outlines how Strands SDK facilitates this by integrating with OpenTelemetry to capture, filter, and export telemetry data, while ensuring sensitive information is managed appropriately. It also highlights the utility of spans in identifying root causes of issues, such as slow response times or tool errors, thereby improving the reliability of AI systems.

Log analysis tools are crucial for efficient incident response by parsing, correlating, and extracting actionable insights from raw log data, which helps engineers quickly identify root causes and reduce mean time to resolution (MTTR). The guide compares five popular log analysis platforms—New Relic, Splunk, Elastic Stack, Datadog, and Sumo Logic—highlighting their strengths in features like AI-assisted anomaly detection, real-time ingestion, and integration with distributed tracing. Each platform offers unique advantages, such as New Relic’s unified database for logs, metrics, events, and traces, and Splunk’s deep search capabilities and historical data retention, catering to different organizational needs. Effective log analysis tools prevent the manual, time-consuming context-switching between multiple dashboards and reduce alert fatigue by surfacing significant patterns and anomalies. Selecting the right tool involves evaluating factors such as scalability, integration depth, and total cost of ownership, ensuring it aligns with the team’s technical requirements and workflow to transform log data into an active driver of rapid incident resolution.

The integration of custom dashboards directly within Catalogs offers a streamlined solution for navigating the increasing complexity of systems, microservices, and cloud environments by providing contextual access to observability data. As the volume of data expands, Catalogs serve as a centralized hub for high-value entities, linking dashboards to specific services, applications, or infrastructure resources, thus simplifying and speeding up access to crucial insights. This approach not only enhances productivity by eliminating the need for users to search across multiple tools but also ensures that the necessary information is readily available during incidents, routine reviews, or onboarding processes. By organizing and curating dashboards ahead of time, teams can benefit continuously, gaining a comprehensive view of their ecosystem and facilitating faster resolution of issues without losing context.

Observability is crucial for engineering leaders to justify their budget spend by demonstrating tangible business outcomes, and New Relic helps in achieving this by focusing on business uptime, digital experience, and engineering excellence. The key benefits of observability include reducing downtime and unplanned outages, enhancing digital user experiences, and boosting engineering efficiency through faster software releases and app modernization. Companies like BlackLine and Intelligent Growth Solutions have significantly cut costs and improved system resilience, while William Hill and The Seven Network have enhanced uptime and digital experiences. Observability empowers organizations to maintain high system reliability, streamline developer productivity, and manage cloud resources effectively, ultimately leading to increased revenue and reduced operational expenditure. New Relic offers tools like the Business Value Calculator to quantify observability's impact, showcasing its importance in delivering high-quality software and digital experiences efficiently.

New Relic Ground Truth addresses the challenge of operational context in AI-powered software operations by transforming telemetry and other data into a unified operational model, enabling AI to function as a trusted operational partner rather than just an intelligent assistant. This approach provides AI with a consistent operational understanding, allowing it to make faster, more accurate recommendations, reduce costs, and lay the groundwork for Autonomous Operations. By creating a reusable layer of operational intelligence, Ground Truth eliminates the need for AI systems to repeatedly reconstruct context, thus enhancing efficiency and reliability. This development represents a significant shift in how software operations are managed, emphasizing shared operational intelligence over sheer data collection, and ultimately aims to improve how AI understands and interacts with complex systems.

Modern software systems produce a vast amount of operational data, providing engineering teams with unprecedented insight into system behavior, yet incident resolution often relies on outdated manual processes. This text discusses the challenges of transforming operational data into actionable decisions, highlighting the role of artificial intelligence and tools like New Relic Autopilot in streamlining this process. New Relic Autopilot is designed to enhance incident resolution by continuously analyzing telemetry, deployment information, and service relationships to recommend evidence-based actions, thus reducing the time engineers spend on investigative work. It emphasizes the importance of operational context and reasoning over traditional automation methods, arguing that the future of software operations will focus on transforming data into faster, more consistent decisions. The text also outlines the journey toward Autonomous Operations, where AI assists in understanding incidents, recommending actions, and eventually automating workflows under human oversight, facilitated by tools like New Relic Ground Truth, which enrich operational understanding with trusted knowledge.

Artificial intelligence is rapidly transforming enterprise technology, with new models and AI assistants frequently emerging to enhance productivity. However, the future of enterprise operations will be shaped not by access to powerful AI models but by the integration of trusted operational context, which includes application dependencies, infrastructure relationships, and historical incidents. As AI models become more commoditized, the unique operational context will become a strategic asset that competitors cannot easily replicate. Observability, providing insights into system operations, must evolve into a framework where AI can be trusted to make autonomous decisions. Operational intelligence, referred to as "Ground Truth," combines real-time data with organizational knowledge to guide AI in making informed decisions. This reliable operational intelligence enables autonomous operations where AI can execute tasks efficiently while human expertise remains central to strategy and innovation. Organizations that successfully integrate this approach can improve reliability, reduce complexity, and expand AI adoption responsibly, marking a shift from traditional observability to a new paradigm of trusted execution.

Dynatrace, known for its robust enterprise observability, faces challenges as environments shift towards cloud-native architectures, leading to complexity and unpredictable costs due to its consumption-based pricing. Many enterprises, managing multiple monitoring tools, experience increased cognitive load and slower incident responses. As such, there is a growing interest in alternatives that offer unified observability, predictable pricing, and reduced operational complexity. Platforms like New Relic, Datadog, Splunk Observability Cloud, Grafana Cloud, and AppDynamics present compelling options. These alternatives focus on consolidating metrics, logs, traces, and events into a unified system, leveraging AI for smarter incident investigation and offering transparent pricing models that align with modern infrastructure needs. The transition to alternatives involves careful consideration of integration with existing workflows, the total cost of ownership, and the ability to handle cloud-native and hybrid environments effectively.

Splunk, a leader in enterprise log management and data analysis, is facing challenges as cloud-native architectures generate increasing telemetry data, leading to budget unpredictability and operational complexity. As a result, many organizations are exploring alternatives that offer unified observability platforms, which consolidate metrics, events, logs, and traces into a single database to improve incident response efficiency and cost predictability. Alternatives like New Relic, Datadog, Elastic (ELK Stack), Dynatrace, and Sumo Logic provide various features such as transparent pricing models, AI-powered insights, and integrations for seamless data correlation across platforms. New Relic, for example, offers a usage-based pricing model with 100 GB of free monthly data ingest, aiming to reduce surprise bills and improve mean time to resolution. Migrating from Splunk typically involves a phased approach, including auditing current usage, planning a migration strategy, mapping queries, and testing with real production data to ensure a smooth transition without disrupting existing workflows.

Intelligent Workloads is an innovative approach designed to assist on-call engineers in managing complex, distributed systems by providing a transaction-centric view of business operations. It aggregates all components involved in critical business flows into a persistent, monitorable entity that adapts dynamically to architectural changes, focusing on complete business transactions rather than isolated services. This solution enhances causal analysis by grouping and monitoring the entire transaction path, helping identify the root cause of issues swiftly. The system offers features like Transaction 360 for comprehensive transaction insights, Business KPIs for linking strategic business insights with observability data, and an AI Summary to prioritize investigation paths during incidents. By using Intelligent Workloads, teams can reduce time spent on manual correlation across different monitoring tools, thus improving efficiency in resolving performance issues.

Site Reliability Engineers and platform developers often recognize performance anti-patterns, such as N+1 database queries and bloated frontend payloads, but many fail to detect these issues in their own production systems due to the resilience of modern applications, which mask inefficiencies. These hidden performance problems can significantly impact infrastructure costs, with inefficient queries potentially wasting thousands annually. To address this, the Performance Risks Inbox has been introduced, offering automated detection of silent anti-patterns across tech stacks, focusing on both cost and speed analyzers. These analyzers identify and prioritize performance risks, helping teams focus on concrete business outcomes by highlighting the financial or user experience benefits of resolving these issues. With zero configuration required, the tool offers immediate value, allowing users to fine-tune settings without code changes, and seamlessly transition from detection to remediation through an interface integrated into New Relic's platform. As architectures evolve, the tool will expand to include additional analyzers, particularly those addressing AI anti-patterns, ensuring platforms remain optimized and future-proof.

Modern digital businesses face the challenge of managing an ever-growing volume of telemetry data across distributed cloud services, which complicates the creation and maintenance of effective alert systems. Traditional alerting methods, which require manual configuration and deep domain expertise, are becoming insufficient in dynamic, cloud-native environments that continuously evolve. New Relic Smart Alerts, now in Public Preview, addresses these challenges by leveraging patent-pending alert intelligence to analyze historical telemetry and recommend alert coverage that aligns with actual system behavior. This approach reduces operational effort, improves monitoring consistency, and minimizes unnecessary alerts, allowing engineering teams to focus on enhancing application reliability and customer experiences. By transforming manual alert configuration into a streamlined process supported by intelligent recommendations, Smart Alerts helps organizations scale observability efficiently and lays the foundation for more autonomous operations.

Grafana, commonly used for visualizing data in observability stacks, often becomes a burden as teams scale due to its reliance on multiple plugins for metrics, logs, and traces, leading to increased operational overhead. The guide explores several alternatives to Grafana, highlighting platforms like New Relic, Datadog, Dynatrace, Splunk Observability Cloud, and Elastic Observability, which offer unified workflows by integrating various telemetry signals into a single system, thereby reducing context switching and enhancing incident response. New Relic is noted for its unified data model that preserves Grafana dashboards, while Datadog excels in cloud integrations, and Dynatrace focuses on AI-driven problem detection. The text emphasizes the importance of evaluating these platforms based on operational overhead, data correlation, and usability rather than just feature count, suggesting a phased migration approach to minimize risk and maintain continuity.