Watch automation failures and open tickets with the cause
Failed runs in your workflows and integrations are collected in one place, grouped by cause, retried where safe and turned into tickets that name the system, the record and the likely fix. For teams who learn that a sync broke when a user complains.
Function: IT · Industry: Any industry
Watch automation failures and open tickets with the cause
6 steps
At a glance
- Trigger
- A workflow run fails or a health check misses.
- Typical inputs
- Run logs and error payloads
- Integration credentials status
- Retry and alerting policy
- Owners by workflow
- Systems it usually connects to
- n8n
- Jira
- Slack
- PagerDuty
- How the work splits
4 rules, 1 model, 1 person steps
How it runs, step by step
6 steps, each tagged by what does the work. Rules are fixed logic, a model reads or drafts, and a person decides.
- Rules Fixed logic in code
- Model AI reads or drafts, with a confidence score
- Person A named person decides
- 1
Collect the failures
RulesErrors from your workflow tools and integrations are gathered with run ID, record and timestamp.
- 2
Group and classify by rule
RulesExpired tokens, rate limits, schema changes and timeouts are recognized from error codes and patterns.
- 3
Retry the safe cases
RulesIdempotent steps are retried with backoff, and nothing that could create a duplicate is retried automatically.
- 4
Explain the failure
ModelA model writes a short cause and suggested fix from the error and the runbook for unrecognized errors.
- 5
Fix and confirm
PersonThe owner fixes the cause and approves a replay of the affected records.
- 6
Review recurring causes
RulesA weekly list shows the most frequent failures so the underlying problem gets fixed.
Where people approve
The decisions that stay with your team, whatever the confidence score says.
- The workflow owner approves replays of failed records.
- Credential changes are made by a person with the right access.
- Alert thresholds are agreed with the business owner.
KPIs to track
What to measure, before and after. Targets come from your own baseline.
- 01Time from failure to the owner being notified
- 02Failures retried successfully without help
- 03Records stuck in a failed state, by age
- 04Recurring failures per month
Questions about this workflow
Why would a model be needed here?
Mostly it is not. Rules handle known errors. The model explains unfamiliar ones in plain language, which speeds up the first look but does not change what the system is allowed to do.
Does this apply to automations built by other vendors?
Yes, if their runs can be logged or called through an API. We tell you during scoping which of your automations we can observe.
Which steps use rules, a model and a person?
Rules: collect the failures; group and classify by rule; retry the safe cases; review recurring causes. Model: explain the failure. Person: fix and confirm. Rules are fixed logic in code. A model reads or drafts and reports its confidence. A named person decides.
What do we need to start?
Typically: run logs and error payloads; integration credentials status; retry and alerting policy; owners by workflow. You also need read access to n8n, Jira, Slack and PagerDuty, or the equivalents you use. A workflow audit measures your volumes and past cases first, then gives a fixed price for the build, so you can decide with real numbers.