April 2026 Summaries

Engineers often face challenges when using the Honeycomb MCP with their AI assistant, primarily due to the "blank page problem" and uncertainty about trusting AI-generated answers without a structured investigative framework. The new Honeycomb MCP course at Honeycomb Academy addresses these issues by providing guidance on initiating queries and critically evaluating AI responses, ensuring that engineers gather evidence rather than just answers. The course begins with an orientation on MCP integration and verification, followed by three independent learning paths—debugging, exploring, and reliability/SLOs—each designed to develop the habit of treating AI output as a starting point for further verification. Participants learn to request details about queries and reasoning, which differentiates effective investigations from those that may be misguided. The course requires a Honeycomb account with telemetry and an AI assistant supporting MCP, and setup takes approximately 20 minutes before users can conduct real investigations with their data.

The text draws a parallel between the atmospheric phenomenon of parhelion, or "sun dogs," and the current state of artificial intelligence (AI) in the tech industry, emphasizing the importance of focusing on genuine user problems rather than superficial metrics of success. It recounts a historical event in 1535 Stockholm, where the appearance of multiple suns in the sky was interpreted as a divine omen during the Protestant Reformation, highlighting the dangers of misinterpreting signs. This serves as a metaphor for the modern-day challenges in software development, where organizations are divided between valuing the end product and the process of creation. The author argues that AI should be integrated as a co-equal partner in development, enhancing both speed and rigor without compromising on quality. The text advocates for the use of observability as a tool to verify and improve software development processes, democratizing both the creation and verification functions and minimizing the risks associated with AI adoption.

In the context of OpenTelemetry and observability, the challenges of semantic convention migrations are exemplified through real-world cases of data inconsistencies due to overlapping old and new attribute names, such as http.url and url.full. These inconsistencies arise because not all services upgrade simultaneously, leading to incomplete results in operational tooling. The OpenTelemetry Collector's schema processor aims to automate attribute name migrations by using schema files that describe changes across versions, ensuring consistent data regardless of instrumentation version. The processor supports both upgrade and downgrade paths, automatically applying the necessary changes to align signal versions with a specified target. However, gaps exist in the current system, such as issues with renames not covered by schema files or signals missing schema URLs, which require additional handling through transform rules. The schema processor is still under development, with ongoing efforts to improve its stability and functionality, such as addressing a bug affecting upgrades and introducing a copy_mode to ease transition periods. While the processor promises to streamline convention migrations, current limitations mean that users need to audit their datasets for attribute divergence and plan for future schema version targets.

Observability in agentic applications is crucial for understanding agent performance and optimizing costs, and it can be achieved by leveraging telemetry through tools like Honeycomb and OpenTelemetry. The text outlines how observability allows for better control over agents by providing visibility into their operations, helping to identify costly or slow models, and enabling precise improvements. It emphasizes the importance of following the Gen AI semantic conventions for telemetry, illustrated through Pydantic AI as an example, to ensure comprehensive trace spans and event capturing. The document also highlights the role of automatic instrumentation in enhancing telemetry and the benefits of using Honeycomb's trace view for detailed insights into agentic operations. Furthermore, it discusses techniques for renaming or transforming telemetry attributes to align with the Gen AI specification and provides practical advice on adding necessary details to telemetry for improving agent visibility. The text concludes by encouraging the use of coding agents to assist in implementing telemetry specifications while ensuring compliance with data privacy regulations.

Traditional observability models, which rely on the "three pillars" of metrics, logs, and traces, are facing challenges in modern service-based architectures, as demonstrated by recent incidents at a large financial institution. These incidents highlighted the limitations of traditional telemetry in assessing customer impact and diagnosing complex interactions between services. The use of OpenTelemetry and Honeycomb has provided more effective solutions, enabling the creation of customer-centric Service Level Objectives (SLOs) that offer a comprehensive view of the customer experience. By tracing single customer requests across services and aligning telemetry data with customer intent, organizations can better diagnose issues and improve customer satisfaction. However, it's crucial to avoid using SLOs to assign blame and instead focus on collaborative efforts between software, product, and business teams to enhance performance and reliability. Contextualizing data through end-to-end traces and involving stakeholders in defining acceptable performance levels are key steps in developing meaningful SLOs that drive continuous improvement.

Honeycomb.io has announced O11yCon 2026, a two-day conference in San Francisco focused on the future of observability, especially as it relates to AI-driven software development. The event, scheduled for May 20-21, will feature hands-on workshops and presentations from notable figures in the tech industry, such as Nathen Harvey from Google Cloud and Charity Majors from Honeycomb, among others. The conference aims to address the challenges posed by AI in the software development lifecycle, including managing AI-generated code and enhancing production reliability. Attendees will gain early access to chapters from the second edition of O'Reilly's "Observability Engineering," which explores how AI is transforming observability practices. O11yCon targets engineering leaders, developers, and platform engineers focused on AI and observability, offering insights into integrating observability with business outcomes. The event is supported by sponsors like AWS and Focused Labs, with early bird ticket pricing available until April 28th.

The rapid evolution of AI in software development and observability is reshaping core principles without altering their essence, as AI-generated code still requires robust observability frameworks to ensure reliability and quality. Despite AI's potential to generate code at unprecedented speeds, the necessity for precise, context-rich feedback loops remains critical to manage the complexity and unpredictability of modern systems. Observability serves as the crucial link between development and production, enabling both human and AI agents to gain insights from data, maintain system integrity, and optimize costs. Tools like Honeycomb provide an advanced observability infrastructure that captures high-dimensional data, supporting AI's ability to discover novel insights and address unforeseen issues that arise in complex systems. As AI becomes integral to infrastructure, the importance of incorporating cost metrics, understanding system behavior, and validating production environments grows, ensuring that AI-driven solutions deliver net value and align with business objectives.

Agentic workloads require precise tooling, similar to developers, needing high cardinality and fast feedback loops to explore their code effectively, but instrumentation tends to be resource-intensive. The growing popularity of Python due to LLM libraries has highlighted performance issues with the OpenTelemetry Python API and SDK, prompting investigations into improving instrumentation efficiency. A benchmarked web service using WSGI and requests libraries was tested, primarily focusing on tracing, revealing the OpenTelemetry SDK's significant memory footprint. OpenTelemetry's API and SDK separation allows for flexible configuration, reducing performance costs and enabling easier swapping of SDK implementations. By leveraging C++ through Pybind11, a prototype was developed to reduce resource usage and address context propagation challenges, demonstrating promising performance improvements with lower CPU and memory footprints compared to existing SDKs. The current implementation is in its early stages and available on GitHub, with an invitation for collaboration to expand its capabilities.

In a webinar with Akshay Utture from Augment Code, the discussion centered around how AI agents utilize production feedback to enhance code quality and efficiency, highlighting the findings from the DORA report on AI's impact on throughput and instability. The conversation explored the importance of integrating observability early in the development process to build trust in AI agents incrementally, similar to onboarding new team members, and emphasized using observability data to make evidence-based decisions rather than relying on intuition. The issue of "agent drift," where AI agents deviate from their intended tasks or produce incorrect outputs, was addressed with solutions like telemetry and output validation, ensuring that humans remain in the loop to prevent significant errors. The text also covered strategies for integrating observability into AI workflows, especially for agents that lack native support, and discussed the balance between learning and execution when delegating code generation to AI, underscoring the role of production telemetry in building system understanding. The traditional software development life cycle (SDLC) was reimagined for the AI era, advocating for a circular model that emphasizes production observation and immediate feedback to accommodate the fast-paced, iterative nature of AI-driven development, with the ultimate goal of creating short feedback loops that enhance performance.

The advent of modern Large Language Models (LLMs) has significantly transformed software development, necessitating a reevaluation of development practices to balance speed and risk. Teams are encouraged to consider the impact of AI on business and product objectives before altering their development methods. Different approaches, such as traditional human-driven practices, supervised agentic development with LLM assistance, and fully agentic processes, offer varied trade-offs between speed and risk. Teams must adapt their practices based on their unique risk tolerances, which can differ greatly even within the same organization. The evolving nature of AI and business environments requires regular reassessment of development practices. Emphasizing the human element, the text advises optimizing workflows for team well-being and leveraging LLMs to automate low-value tasks, maintain documentation, and improve quality of life for developers. Ultimately, the text underscores the importance of trust and flexibility in navigating the rapidly changing landscape of software engineering.

The era of AI agents is transforming engineering teams as they deploy multi-agent systems and seek to understand observability in non-deterministic systems, with Honeycomb offering solutions to these challenges. Honeycomb's tools provide comprehensive visibility into AI model performance, costs, and user outcomes, allowing teams to compare agent versions and identify issues such as latency, failures, or unexpected costs. By integrating with AI evaluation frameworks like Braintrust, Honeycomb presents both objective metrics and subjective quality signals, enabling engineers to detect problems like hallucinations or bias before they affect users. The platform utilizes distributed tracing to highlight inefficiencies and misconfigurations, such as unnecessary latencies or timeout errors, providing actionable insights for optimization. Honeycomb's approach empowers teams to diagnose and resolve issues in AI systems efficiently, paving the way for improved agentic observability and facilitating better-informed development cycles.

Honeycomb provides a robust observability platform designed to support advanced AI capabilities and extensive data analysis, enabling rapid problem diagnosis and remediation in production environments. By utilizing a unique data model, query engine, and telemetry infrastructure, Honeycomb allows for quick detection of deployment issues, offering insights within 90 seconds of an event occurring. This is facilitated by features like BubbleUp for instant correlation analysis and SLO alerting. Additionally, the platform supports business-specific data instrumentation, enabling telemetry to reflect business impacts such as revenue and conversion rates. Honeycomb's Telemetry Pipeline intelligently manages data storage and privacy through full-fidelity archival, smart sampling, and PII redaction, ensuring cost-effective and comprehensive data management. The platform's automation capabilities, such as Kubernetes auto-remediation, enhance operational efficiency by responding to issues in real-time. Honeycomb's design caters to the high-dimensionality data required in modern software engineering, making it a powerful tool for the AI era and beyond.

Honeycomb's observability platform enhances AI agents' ability to diagnose and resolve production issues by providing comprehensive visibility into system performance, enabling agents to query data, identify bugs, and propose fixes within an integrated development environment. The Honeycomb Model Context Protocol (MCP) offers a robust interface that supports various tools and agents, facilitating a seamless workflow for automated troubleshooting and remediation. The Canvas feature acts as a collaborative investigative workspace, enabling teams to form hypotheses, conduct structured investigations, and review evidence in real-time. Autonomous functions like creating service level objectives (SLOs) and assessing application performance are performed by AI agents, but human oversight is maintained to authorize actions, ensuring safe and effective operations. The platform's capabilities are demonstrated through scenarios involving performance assessment, SLO creation, and incident investigation, showcasing its utility in streamlining production workflows and enhancing team collaboration.

KubeCon + CloudNativeCon Europe highlighted the transformative influence of AI on software development and the critical role of observability in navigating this shift. Participants emphasized the importance of strong feedback loops and observability tools to manage AI-induced complexities, such as non-deterministic behaviors in large language models (LLMs) and the disconnect between frontend and backend traces. Sessions discussed redefining key performance indicators for AI systems, leveraging AI coding agents for OpenTelemetry adoption, and simplifying configuration through declarative methods. The event also explored challenges like maintaining telemetry accuracy under data limits and responsibly using AI in open source contributions. A recurring theme was that, while AI can enhance engineering processes, without effective observability and feedback mechanisms, it risks exacerbating operational challenges.

A career driven by empowering others and achieving positive engineering outcomes has led the author to embrace observability as a fundamental practice for sustainable software systems management. Initially working in ops engineering at Amperity, the author was introduced to Honeycomb's observability principles, which shifted their perspective and fueled advocacy for improved observability practices. By focusing on outcomes and fostering a collaborative environment without immediately pushing specific tools, the author facilitated the adoption of Honeycomb after a thorough, data-driven evaluation. This adoption was supported by a broad coalition of engineering teams and resulted in significant improvements to production excellence and reduced on-call burdens. These experiences culminated in the author's eventual role at Honeycomb, where they continue to apply their principles of compassionate problem-solving and advocacy for a culture of continuous learning and improvement.