July 2026 Summaries

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.