The Problem Wasn’t Triage

Most AI workflows in software engineering still keep the human directly in the middle of triage.

The AI might help write code.

It might explain a stack trace.

It might summarise a pull request.

But the operational loop itself still depends on someone noticing the issue, prioritising it, investigating it, and deciding whether it matters.

I recently shipped a new feature in one of my side projects. The feature itself worked well and was tested properly, but a reasonably significant crash slipped through because it never crossed the notification thresholds in Firebase Crashlytics.

Had I not gone looking manually, I probably would never have known.

That was the moment the idea started forming.

Building the Workflow

Hermes already had access to Crashlytics through MCP tooling, so I started experimenting with whether the entire discovery and investigation process could be automated.

The workflow now looks roughly like this:

Hermes checks Crashlytics every 4 hours

New issues…