SUMMARIZATION
Weekly regression-trend brief from warehoused Sentry data
Each week, reads deploy and Sentry error history from BigQuery, clusters recurring regressions by root-cause signature across all releases.
How it runs
The automated pipeline, trigger to output.
- TriggerWeekly schedule
- ActionQuery BigQuery for deploys joined to Sentry errorsBigQuery
- LogicCluster regressions by root cause across releases
- LogicRank clusters by recurrence and impact
- ActionWrite recurring-regression trend briefOpenAI
- OutputPost trend brief to engineering SlackSlack
What it does
Looks past single deploys to find patterns. It reads a week of warehoused deploy and Sentry error records, clusters regressions by root-cause signature across every release, and surfaces the causes that keep coming back, so the team can fix the systemic issue instead of patching the same break repeatedly.
When to use it
When individual deploy alerts are handled but nobody sees the bigger picture of which subsystems regress over and over. Use it for a weekly engineering retro input or reliability review.
How it works
- 1A weekly schedule triggers the trend analysis.
- 2Query BigQuery for the week's deploys joined to their Sentry error groups.
- 3Cluster regressions by root-cause signature across all releases in range.
- 4Rank clusters by recurrence and total user impact over the week.
- 5An LLM writes a trend brief naming repeat offenders and affected areas.
- 6Post the brief to the engineering Slack channel.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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