Fast cause hypotheses pull against the evidence engineers need to trust them.
Issue Detection needs shorter investigations without hiding the event chain that explains an issue.
Who you’d be doing this for
“I had three issue links open and still couldn't tell which event changed the story.”
Ashwin Verma · Application Developer
Uses issue details to decide where to begin when a production failure reaches their service.
What is at stake
Issue Detection engineers are reconstructing issue creation across Kafka and PostgreSQL by hand. You must decide what AI can rank and what engineers must verify before it changes their investigation flow.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- faster investigation vs. inspectable evidence
- ranked hypotheses vs. engineer judgment
- retrieval breadth vs. production load
- reusable workflow vs. narrow pilot scope
Why Sentry
Issue Detection turns production telemetry into the issues developers use to find and fix code-level failures.
Written with these in mind
Not your kind of problem? 20 more at Sentry, or browse every organization.
This is the setup. The work is inside.
Running it puts you in the room: the full situation and its constraints, stakeholders who push back in their own words, and the decisions that are yours to make. What you produce becomes a Day One Plan — work you can show someone instead of describing.