DATA OPS
Dead-Letter Triage for BigQuery-to-Intercom Sync
Pulls failed records from a BigQuery dead-letter table, uses an LLM to diagnose why each row was rejected by Intercom, groups them by root cause.
How it runs
The automated pipeline, trigger to output.
- TriggerScheduled dead-letter scan
- ActionQuery unprocessed rows from BigQuery dead-letter tableBigQuery
- ActionLLM diagnoses and categorizes each failureOpenAI
- LogicGroup rows by root cause and count
- OutputFile one consolidated Linear issue per root causeLinear
- ActionMark processed rows resolved in BigQueryBigQuery
What it does
A pile of dead-lettered sync rows is useless until someone explains the failures. This workflow reads the dead-letter table from a BigQuery-to-Intercom reverse-ETL job, asks an LLM to read each error and bucket it into a human-readable root cause (bad email format, unknown company, schema mismatch), then opens one consolidated Linear issue per root cause rather than spamming a ticket per row.
When to use it
Reach for this when your dead-letter table accumulates dozens of cryptic API errors and your team needs them turned into actionable, deduplicated engineering tasks instead of a raw error dump.
How it works
A schedule trigger queries the BigQuery dead-letter table for unprocessed rows. An OpenAI step classifies each row's error message into a normalized root-cause category with a plain-English explanation. A logic step groups rows by category and counts them. For each distinct root cause, a Linear issue is created listing affected record ids and a suggested fix. Processed rows are then marked resolved in BigQuery so the next run starts clean.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect LinearIssues, projects, cycles, triage.
- 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 it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
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