January 2026 Summaries

Enterprise software has evolved from testing generative AI models to integrating them into product interfaces and infrastructure, driven by the need for competitive advantage. This shift, similar to the adoption of microservices, involves transforming the technology stack to accommodate the varied inputs and outputs of large language models (LLMs), which increases the risk of system failures due to their probabilistic nature. AI reliability and observability have become critical concerns, with predictions that by 2028, 40% of organizations will implement AI observability to manage new failure patterns such as hallucinations, model drift, and latency issues. Traditional observability platforms struggle with the complexity of AI telemetry, prompting the development of specialized tools that can track high-dimensional data for real-time insights. Companies like Intercom have successfully leveraged these tools to optimize performance and costs, as seen with their AI agent Fin.ai, which improved response times and scalability through detailed monitoring and data analysis. To ensure safe scaling of generative AI features, platforms must offer unlimited cardinality and high dimensionality, enabling engineers to discern meaningful signals from the noise of high-volume AI workloads.

At Honeycomb, the engineering team has been utilizing Claude Code, and efforts have been made to measure its adoption, usage, and cost-effectiveness through telemetry integration with Honeycomb's analytics platform. By configuring Claude Code to send logs and metrics via OpenTelemetry, the team was able to monitor its usage within their primary monorepo and gain insights into API requests, tool executions, user interactions, and error occurrences. To facilitate comprehensive monitoring, the Honeycomb MCP server was set up, allowing Claude Code to autonomously generate a dashboard that answers key questions regarding user engagement, cost management, developer experience, and performance reliability. This setup not only enabled the team to track adoption rates and expenditures but also highlighted tools' usage patterns and potential issues, such as API latency or tool execution rejections. For users seeking a simpler setup, Honeycomb offers pre-built board templates tailored to monitor Claude Code's ROI, cost, usage, and performance metrics.

Honeycomb, a platform designed to help users understand custom software, initially faced challenges due to its vast array of customizable fields and visualizations, but it has evolved to incorporate AI-driven tools like Canvas and the Honeycomb MCP to enhance data observability and problem-solving. Canvas, Honeycomb's built-in agent, allows users to query the status of their applications in real-time, efficiently identifying issues such as latency spikes by examining high-level measurements like errors and request durations. The platform's integration with agentic AI enables rapid querying and detailed analysis by utilizing structured logs and trace spans, ultimately assisting in pinpointing the root causes of problems and suggesting code changes. By combining observability data with code insights, Honeycomb offers a robust, AI-powered solution to streamline the debugging process and improve application performance, fulfilling a long-standing desire for integration within development environments.

The text explores the evolution of DevOps and the role of feedback loops in software development, highlighting that while the DevOps movement aimed to connect developers with production systems, it fell short due to inadequate technology. The advent of AI is seen as a game-changer, offering new possibilities for creating effective feedback loops between developers and production systems, thus overcoming previous technological limitations. The text emphasizes the importance of observability in closing feedback loops and suggests that AI has significantly reduced the complexity and cost of instrumentation, allowing developers to focus more on experimentation and validation rather than merely writing code. It also points to a shift in the developer's role, where understanding and decision-making become more crucial than code writing itself. Despite these advancements, the text acknowledges that many organizations still rely on outdated operational feedback loops, potentially leading to challenges in understanding and managing AI-generated code. Ultimately, the piece argues that while DevOps is not dead, the new AI-driven landscape offers opportunities for improvement by better connecting developers with the outcomes of their work in production environments.

OpenTelemetry has developed the OpenTelemetry Agent Management Protocol (OpAMP) to address the operational challenges of managing large-scale telemetry infrastructures. As systems scale, the management of telemetry agents, particularly the OpenTelemetry Collector, becomes complex, necessitating consistent configurations, health monitoring, and safe deployment changes. OpAMP provides a vendor-neutral, OpenTelemetry-first specification designed to manage telemetry agents efficiently by establishing a control plane for remote configuration, health reporting, and extensibility. The protocol involves a client-server model where clients, like the Collector, communicate with a server to announce their presence, report status, and receive configuration updates. This communication is typically conducted over WebSocket or plain HTTP and allows for dynamic interaction between clients and servers based on their capabilities. OpAMP standardizes the management of telemetry agents, making it easier to observe and operate extensive telemetry infrastructures securely. Honeycomb Telemetry Pipeline utilizes OpAMP for its agent management, emphasizing the importance of this open-source solution within the OpenTelemetry ecosystem for improving observability and scalability.

Honeycomb's implementation as a distributed column store, rather than a time series database (TSDB), addresses limitations inherent to TSDBs, particularly in handling high-cardinality data and maintaining raw data for contextual insights. While TSDBs like Facebook Gorilla offer efficient storage and retrieval of time-stamped data through advanced compression algorithms, they fall short in providing true observability due to their reliance on pre-aggregated metrics and limited capacity to manage high-cardinality tags. Honeycomb, in contrast, optimizes for storing raw, high-cardinality data, allowing for comprehensive analysis by computing aggregates only at query time, thus preserving all original data. This design supports both metrics and tracing, enabling a more nuanced view of system behavior without the constraints of a TSDB's structure. Ultimately, Honeycomb's approach offers a more flexible and contextualized solution for observability, while acknowledging the trade-offs between the specialized efficiencies of TSDBs and the broader capabilities of distributed column stores.

In a recent webinar, Charity Majors and James Governor discussed the challenges and opportunities of managing observability budgets, emphasizing that while the costs can seem exorbitant, effective observability is crucial for driving business outcomes. Modern systems generate vast amounts of data, and the complexity of these systems requires observability tools to do more than just indicate system uptime; they should enhance product understanding and contribute to revenue growth. Charity highlighted the importance of both external and internal observability, with the latter having significant untapped potential for improving developer experience and speed of deployment, as exemplified by Intercom's rapid deployment model. The conversation also touched on the transformative potential of AI, which could allow developers to interact with systems through natural language, integrating observability more seamlessly into their workflow. Ultimately, the discussion framed observability as an investment in a growth engine rather than a mere operational cost, essential for everyone from developers to executives to understand and optimize modern systems effectively.