February 2026 Summaries

Honeycomb's disaster recovery exercise on December 5th, which involved intentionally destroying Kafka brokers as part of the process, led to significant disruptions in their production environment. Though previous pre-production tests had run smoothly, the production incident caused multiple Kafka topic partitions to go leaderless, resulting in damaged data and operational challenges. The company's response involved identifying impacted teams, salvaging partitions, and managing disk space concerns. Over the following weeks, Honeycomb undertook a complex recovery process, including the temporary shutdown of tiered storage to free disk space, manual repairs of Kafka topics, and ultimately planning a complete migration to a new Kafka cluster. The incident revealed issues with documentation, communication, and sociotechnical alignment within the organization, prompting reflections on improving incident management and knowledge sharing. This extensive effort highlighted the importance of strategic planning and adaptability in response to unforeseen technological challenges.

Google's Core Web Vitals (CWVs) are essential metrics used to assess the performance of web pages and have influenced Google's page rankings since 2021. They include Time to First Byte (TTFB), First Contentful Paint (FCP), Cumulative Layout Shift (CLS), Largest Contentful Paint (LCP), and Interaction to Next Paint (INP), each providing insights into different aspects of user experience and page performance. Honeycomb enhances frontend observability by integrating CWVs into its telemetry system, allowing web administrators to analyze these metrics in detail through trace spans, which include various attributes like browser and device details, page navigation, and session data. While CWVs are useful for understanding web performance, they are not always the most critical metrics for single-page applications, where client-side route changes and user interactions can be significant. Honeycomb's OpenTelemetry-based SDK enables end-to-end tracing that connects browser activities with backend operations, offering a comprehensive view of application behavior. By combining these insights with CWVs, developers can better diagnose issues and optimize frontend applications for improved user experience.

In a webinar featuring Honeycomb's co-founders Christine Yen and Charity Majors, the evolution of observability and its intersection with AI was discussed, reflecting on the foundational gaps between development and operations that necessitated closer integration of observability with code. The conversation explored the origins of the "three pillars" of observability and highlighted that true understanding of systems goes beyond mere telemetry organization. AI introduces challenges by disrupting deterministic software models, yet it may also bridge gaps between current operational tools and developer feedback needs. Charity Majors emphasized the challenges of achieving observability portability across vendors due to inherent operational lock-in and the importance of building migratable systems to manage technical debt. On the management front, she noted the fragility of formal management roles in an AI-augmented engineering world and advised that management should not be an early career path due to the rapid decay of technical skills. The discussion also touched on the potential of data lake houses to enhance observability by preserving context and avoiding data silos, and the enduring relevance of logs platforms. Additionally, Charity pointed out the necessity of tying observability investments to business outcomes, treating them as strategic investments for revenue-generating activities, and critiqued the static nature of dashboards as increasingly obsolete in the dynamic data landscape.

In February, Refinery released version 3.1 of its tail-based sampling solution, offering performance enhancements, bug fixes, and new telemetry features. Accompanying this release was a new tool for the MCP server, designed to help AI systems understand Refinery and Honeycomb's sampling processes. The tool allows users to query AI assistants about their sampling rules, configuration, and sample rates. During testing, the AI assistant identified a critical issue in the sampling rules, specifically a misconfiguration in the end-to-end (E2E) data rule due to an unquoted integer value, which led to incorrect sampling rates. This discovery underscored the AI's capability in pattern-matching and problem-solving when integrated with Honeycomb's MCP server. The experience highlighted the importance of understanding rule configurations and the potential for AI tools to improve data management practices by identifying and diagnosing issues that might otherwise go unnoticed.

In 2016, Christine and the author founded Honeycomb, aiming to revolutionize software observability tools by drawing insights from their experiences at Facebook and emphasizing context-rich, exploratory data analysis. They identified that existing tools struggled with increasing complexity, initially in areas like cloud and microservices, and now with AI's rapid code generation, which shifts the challenge from coding to learning and validating. The narrative stresses that while AI accelerates development, it also requires deep, contextual data to function effectively. The text critiques traditional observability tools for fragmenting data across silos, advocating for systems that integrate context to handle unpredictable AI behaviors. It emphasizes the need for fast feedback loops, flexible tooling, and a shift from human-led queries to AI-assisted exploration. The document argues that operational excellence remains crucial in the AI era, highlighting that the future of software engineering involves blending human expertise with AI capabilities to unlock greater value and efficiency.

As the adoption of AI-code generation and agent-based features increases, software development and instrumentation are evolving, with OpenTelemetry emerging as a standard instrumentation layer across enterprises. This shift emphasizes the need for rich context and granularity in telemetry data, driven by factors such as the integration of AI features and the proliferation of codependent services. OpenTelemetry offers flexibility by allowing teams to capture relevant context without being locked into specific observability backends, though many observability solutions incur high costs for extensive data tagging and exploration. Honeycomb addresses these challenges by providing an OpenTelemetry-native platform that scales with high-cardinality data, offering seamless integration, reduced overhead concerns, and empowering developers with real-time performance feedback. It enables enterprises to capture custom context without economic constraints, supporting the maturation of OpenTelemetry adoption and allowing engineering leaders to focus on strategic development rather than economic trade-offs in observability.