July 2026 Summaries

Honeycomb has been recognized for its innovative approach to observability, emphasizing its fast, flexible, high-cardinality querying that allows engineers to understand not just when something breaks, but why. As the software development lifecycle (SDLC) has significantly compressed, understanding real-time software performance has become crucial, especially in an era where AI-generated code and unscripted agent decisions are common. Honeycomb's tools, such as the Agent Timeline and Honeycomb Canvas, help engineers trace and diagnose the root causes of issues by organizing data around events like agent invocations and tool calls. The platform's event-based pricing model encourages the collection of rich telemetry data without unpredictable costs, setting it apart in the field of observability. Honeycomb's approach aligns with the future of software development, focusing on AI-native and event-driven systems, and has been recognized for its depth in addressing agent audit-trace problems.

In navigating the challenges presented by Honeycomb's Private Cloud offering, the Tenant team undertook an experiment to address the bottleneck in their code review process by leaning into it rather than trying to eliminate it. This approach involved focusing on reviewing not just the code but also the plans and prompts given to AI tools responsible for code generation. The team aimed to increase awareness and alignment among members by utilizing a standardized process for planning and feedback, which included creating Claude skills for various project phases. Despite initial reluctance, the experiment fostered greater team cohesion and understanding of the system, leading to shared mental models and the ability to reuse plans for recurring tasks. Although the strict form of the workflow was not universally applied to all tasks, its principles were selectively adopted, contributing to improved planning and alignment without compromising development velocity. Overall, the initiative highlighted the potential of AI-assisted programming while emphasizing the importance of team dynamics and the qualitative aspects of software development.

Over the past year, advancements in AI have significantly impacted systems design and operations, as initially predicted in a prior blog post. AI agents have demonstrated their ability to perform complex tasks, such as zero-shot investigations, more efficiently and cost-effectively than before, with agent-initiated queries now being processed at large volumes through platforms like Honeycomb. Despite these advancements, human involvement remains crucial, as AI tools are enhancing rather than replacing human capabilities, allowing for more complex and productive queries. The cost of AI operations is decreasing, with agent queries becoming more resource-efficient compared to human-led ones, though they pose challenges like reduced caching efficiency. Fast feedback loops are emphasized as critical to development and operational success, with AI enabling rapid verification and problem-solving through automated processes. Honeycomb is focused on creating tools that facilitate these feedback loops and enhance understanding of AI's interactions with code, with initiatives like the Canvas Agent showcasing their evolving capabilities.

Honeycomb's engineering team shares insights on how they significantly increased throughput using AI, emphasizing the importance of a solid organizational framework of autonomy, ownership, and feedback loops. They highlight that AI can exacerbate issues in dysfunctional organizations but enhance productivity in well-structured ones. The team stresses the need for continuous delivery, robust feedback mechanisms, and accountability practices, such as CLAUDE.md and skills, to ensure AI-driven work maintains quality. They advocate for fast, AI-legible CI systems and closed-loop observability to monitor the real-world impact of AI-shipped code. The post underscores the importance of platform investments, such as feature flagging and maintaining a dissemination layer to keep stakeholders informed of AI-driven changes. Honeycomb cautions that AI should not replace human judgment and emphasizes the need for sustainable practices and cultural shifts to fully leverage AI's potential without sacrificing quality or accountability.

In a detailed two-part blog series, the author explains how Honeycomb's engineering team notably increased its throughput using AI, achieving a 2.5x productivity boost without compromising quality. This accomplishment unfolded in three phases: initial experimentation in 2025, a tooling-driven adoption surge in October 2025, and intensified delegation following the release of Opus 4.6 in February 2026. The blog emphasizes that AI amplifies existing practices, enhancing productivity in organizations with high autonomy and ownership while exacerbating dysfunction in less organized environments. The analysis highlights the importance of continuous delivery, fast and AI-readable CI, and robust observability as key factors enabling AI-driven success. The narrative also underscores the complexity of attributing productivity gains solely to AI adoption, emphasizing the need for supporting infrastructure and strategic alignment. Honeycomb's journey parallels Intercom’s experiences, both organizations recognizing that AI's role is to amplify existing capabilities rather than serve as a standalone solution for productivity enhancements.

Honeycomb recently completed a significant Kafka migration, transitioning from a self-hosted Confluent Platform and ZooKeeper to open-source Apache Kafka 4.1.1 in KRaft mode on AWS EKS, driven by the need for technical capability and sustainability, particularly for Honeycomb Private Cloud installations. The migration was meticulously planned, emphasizing lessons learned from past incidents, ensuring rollback safety, and maintaining data integrity and continuity, which were critical due to the complex data processing requirements managed by Kafka at Honeycomb. The project highlighted the importance of cross-team collaboration, building on institutional knowledge, and fostering team competence and confidence through detailed documentation and iterative learning from each migration phase. By designing a generalized and repeatable migration process, Honeycomb not only ensured the project’s success but also empowered their team, enabling them to manage future migrations with increased autonomy and expertise. Despite the challenges, the migration served as a testament to the team’s ability to tackle complex projects and the significance of clear communication and robust planning in achieving organizational goals.

In a rapidly evolving tech landscape, software engineering is facing unprecedented challenges, particularly with the integration of AI and the ongoing struggle with observability issues. Despite the expectation that AI would be a panacea, it has instead amplified existing problems, such as burnout and competition pressures, while adding new complexities like probabilistic behaviors in software systems. The book "Observability Engineering" addresses these challenges by emphasizing the importance of understanding and learning within organizations, especially for technical decision-makers. It underscores the need for engineering rigor in an AI-driven era and presents strategies for improving observability as a critical factor for success. A key message is that while AI tools can enhance understanding and efficiency, the core skill remains the ability to comprehend complex systems and customer needs effectively. Darragh Curran, CTO of Fin, shares insights from his experience leading an AI-first transformation, highlighting that the true bottleneck in engineering today is understanding, not shipping or coding. This understanding is essential for making informed decisions and iterating rapidly in an era where AI systems introduce both opportunities and uncertainties.

Platform teams are increasingly challenged by the surge in pull requests (PRs) that introduce system drift, making it crucial to establish robust guardrails for swift product development and issue detection. Linters are valuable for flagging basic deviations due to their efficiency and integration into continuous integration (CI) processes, but they lack flexibility for nuanced code evaluations. To address this, AI-assisted code reviews offer a high-context analysis, complementing linters by capturing complex issues with contextual understanding. At Honeycomb, the Lattice Watch system was developed to enhance design system adherence using AI reviews, which post detailed feedback on PRs and track telemetry data to identify trends and areas for improvement. Visualized through Honeycomb Canvas, this data-driven approach enables the identification of common deviations and necessary component updates, fostering a culture of improvement and ease of use. This replicable model, combining linters, AI reviews, and telemetry, can be adapted to other domains like security, accessibility, and performance, ensuring systems remain aligned as automated coding increases.

Monitoring and observability serve distinct roles in system management, with the former focusing on predefined alerts for known issues and the latter enabling investigation of unexpected behaviors, which is crucial in AI-driven environments where unpredictable failures often occur. Traditional monitoring, which relies on static dashboards and predefined metrics, struggles with AI systems due to their non-deterministic nature, where identical inputs can yield different outputs and system behavior can shift without direct code changes. Observability, on the other hand, offers a more dynamic approach by capturing comprehensive request-level data, facilitating post-failure inquiries and enabling teams to learn and adapt from production incidents. This distinction is particularly vital in AI contexts where failure modes are novel and cannot be fully anticipated or tested before deployment, necessitating a shift towards an observability-first approach that emphasizes distributed tracing and incident-driven learning. Engineering teams must prioritize high-risk AI user journeys and build competencies in investigation-ready instrumentation and cross-team debugging to effectively manage the complexity and unpredictability of AI workloads, with platforms like Honeycomb providing the necessary tools to achieve these goals by offering unified visibility and faster incident resolution.

AI applications generate more than just model outputs, involving various elements like prompts, retrievals, tool calls, and latency, which are crucial for understanding performance and costs. To manage production issues, engineering teams require comprehensive AI observability tools that consolidate telemetry data for troubleshooting, model behavior evaluation, and cost optimization. With a variety of tools available, each catering to different needs such as full-stack observability, LLM evaluation, cost tracking, or model governance, the choice depends on factors like use case, OpenTelemetry compatibility, and deployment model. Effective AI observability platforms provide insights into what happened, why it happened, and at what cost, helping teams make informed decisions despite the complexity and non-deterministic nature of AI systems. Key features include tracing, token usage tracking, evaluation workflows, and OpenTelemetry integration, ensuring that AI telemetry is seamlessly connected to broader production systems. Honeycomb, as an example, offers production-level visibility that integrates AI behavior with services and infrastructure, highlighting the importance of a multi-layered observability stack that combines AI-specific tools with broader system monitoring.

Honeycomb Canvas is a collaborative debugging platform that allows multiple engineers to investigate production issues simultaneously, each assisted by their own AI agent. This setup fosters a unique blend of independence and collaboration, as agents operate autonomously but can observe and build upon each other's findings through a shared coordination context. Canvas achieves this by establishing a multitenant architecture, where agents maintain separate LLM session contexts within a common environment while leveraging a collaboration plane that tracks hypotheses, activities, findings, and peer communications. This framework supports two distinct investigation patterns: directed investigations, where a parent agent coordinates the work of subagents, and cooperative investigations, where multiple users and agents independently pursue their lines of inquiry informed by the collective knowledge. By modeling the natural learning behavior of collaborative problem-solving, Canvas enhances the efficiency and effectiveness of incident debugging, supported by sophisticated observability tools that trace coordination decisions to ensure optimal outcomes.

Martin Holman co-authored a post detailing the creation of Honeycomb Canvas, a collaborative investigation platform utilizing AI agents to assist users in understanding and troubleshooting systems. The platform is built on AWS Bedrock AgentCore, chosen for its modularity and production-grade capabilities, although it presents certain challenges. The post discusses the design decisions made in building Canvas, particularly the management of state across various layers to ensure continuity across sessions. AWS AgentCore's Runtime platform provides isolated compute environments, but its session storage is limited to the current version, prompting the use of S3 Files for persistent session history. Deployment strategies were adjusted to maintain session continuity by linking investigations to specific Runtime versions. Observability is emphasized, with Honeycomb's tools used to monitor agent performance, ensuring a robust agentic application. The post indicates further exploration into Canvas's architecture in an upcoming part of the series, focusing on its collaborative design and agentic investigation capabilities.

Reflecting on 2.5 years of managing Site Reliability Engineering (SRE) teams, the author discusses the challenges and learning experiences encountered during the first year, which laid a foundation for managing multiple teams, including Honeycomb Private Cloud. Initially intimidated by the demands of leading an engineering team, the author navigated through various obstacles by learning to run effective meetings, providing timely feedback, and understanding the nuanced role of a manager. Emphasizing the importance of building trust, seeking feedback, and striking a balance between involvement and delegation, the author highlights the need for continuous learning and adaptation in management. Additionally, the piece suggests that managers should cultivate strong relationships, actively seek feedback, and prioritize asking questions over giving directives to thrive in their roles.