Blog

Thoughts on building AI agents, incident response, and developer tools.

When software breaks at 2am, every minute costs thousands.

Software teams spend 30-40% of their engineering time investigating what broke and why. We built the system that does it automatically, with 78% root cause accuracy on 200 real production bugs.
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How we outperformed Claude Code and Codex on root cause accuracy

We benchmarked 200 production bugs across 12 engineering teams (3-12 engineers each). All three systems get identical observability MCP tools and full repo access. Sonarly and Claude Code run Claude Opus 4.6, and we compared against OpenAI Codex (GPT-5.3) as a second baseline. The difference is the Context Graph, self-contradiction, and bug reproduction.
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How I stopped my agent from being lazy

A simple output format trick that forces AI agents to actually investigate before concluding. Based on a real production incident where 4 out of 5 analyses were wrong.
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How my agent built its own tool

A technique I stumbled on while building Sonarly that changed how I think about agent design.
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