TICKET MANAGEMENT
Correlate Front and Sentry Signals into a Notion Problem Brief
When a Front ticket cluster and a Sentry error spike point at the same feature within the same window, an agent links the two signals and publishes a correlated Notion problem…
How it runs
The automated pipeline, trigger to output.
- TriggerFront cluster or Sentry spike event
- ActionRecord signal with feature area to PostgresPostgres
- LogicCorrelate against opposite signal in same windowPostgres
- LogicNo correlation -> exit
- ActionAgent assembles correlated problem briefOpenAI
- OutputPublish brief to Notion and announce in SlackNotion
What it does
Connects what customers report with what the system actually breaks. The workflow keeps a short-lived window of recent Front ticket clusters and Sentry error spikes in Postgres, and when both point at the same feature area inside the same time window an agent writes a Notion problem brief that joins the customer-facing symptoms to the underlying error fingerprint and impacted releases.
When to use it
Use it when support and engineering see the same incident from two sides but never join the dots quickly. Ideal for teams that want a single authoritative problem brief correlating customer impact with technical root cause, ready for an incident review.
How it works
- 1A Front cluster or Sentry spike event triggers the run.
- 2The signal is recorded with its feature area and timestamp in Postgres.
- 3A correlation query looks for a matching opposite-type signal in the same window and feature.
- 4If no correlation is found, the run ends.
- 5On a match, an agent assembles a problem brief joining customer symptoms to the error fingerprint and releases.
- 6The brief is published to Notion and announced in a Slack channel.
Set it up
What you configure once, before turning it on.
- 1Connect FrontShared inbox, conversations.
- 2Connect SentryErrors, performance, releases.
- 3Connect PostgresAny Postgres URL — query, write, migrate.
- 4Connect NotionPages, databases, comments.
- 5Connect SlackChannels, DMs, threads, mentions.
- 6Connect OpenAIModels, embeddings, files.
- 7Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 8Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 9Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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