Operations | Monitoring | ITSM | DevOps | Cloud

Honeycomb recently launched Canvas connectors, with Checkly serving among its first group of integration partners. With the Honeycomb Canvas connector for Checkly, the Canvas Agent can bring your synthetic monitoring into an investigation to help determine which checks are failing, from which locations, since when, and what Rocky AI identified as the likely root cause.

Most people investing in software factories fail to close the loop. Agents write, review, test and ship code, but then the factory loop ends. What happens after deploy? Nobody knows.

Our Results Daemon processes about 92 million messages a day. We recently rewrote it from Node.js to Go, and we let Claude Code write it. We wanted to know whether we could trust an agentic rewrite for a critical, high-throughput production service rather than a prototype. It shipped with zero incidents, a 70% reduction in running pods, and a lighter database load.

MCP servers have a reputation: they eat your context window. CLIs paired with skills, on the other hand, are more token efficient. But is this still true? I dropped all my MCP servers five months ago. Five months is a long time in AI land. When Anthropic came up with the concept of skills, many people stopped using MCP servers in favor of CLI tooling and skills.