Exception Handling and Monitoring
Queues, alerts, retries, dead-letter handling and a dashboard that tells you something broke before your customer does.
Queues, alerts, retries, dead-letter handling and a dashboard that tells you something broke before your customer does.
Most automation is built for the happy path. The exceptions land in someone's inbox, or nowhere at all.
A flow fails at 2 a.m., the error goes to a mailbox nobody reads, and the first alert is a customer asking where their order is.
Cases the automation can't handle pile up with no queue, no owner and no deadline.
Nobody can say how many items went through today, how many failed, or whether the SLA is being met.
Every automation we build reports on itself. We add the same to automations you already run.
Every case the automation can't finish lands in a queue with an owner, a reason and a due date.
Temporary failures retried automatically with sensible delays, so a brief outage doesn't become a manual cleanup.
Items that keep failing are parked safely in a dead-letter queue with full context, ready to fix and replay.
Alerts on error spikes, volume drops and SLA risk, sent to the right person in the tool they already use.
Volumes, success rate, exception rate, backlog age and SLA performance on one screen.
Every exception tagged by cause, so the top reasons get fixed upstream instead of handled forever.
We start with what is already running.
List every automation and integration, where it logs, and who hears about failures today.
Retries, queues, owners and SLAs for each failure type, agreed with the people who will work the queue.
A live dashboard plus a short weekly review of the top exception causes, until they shrink.
| Term | Meaning |
|---|---|
| Exception | A case the automation can't complete on its own, such as missing data, a rule conflict or a system error |
| Retry with backoff | Trying a failed step again after increasing delays, to ride out short outages |
| Dead-letter queue | A holding area for items that failed repeatedly, kept with full context so they can be fixed and replayed |
| SLA (service level agreement) | The agreed time or quality target, such as orders processed within four business hours |
| KPI dashboard | A live view of the few numbers that show whether the process is healthy |
| Observability | Logs, metrics and traces that let you see what a system did and why |
System Integration · Agentic Workflows · Documentation & Handover
Because error handling is often an afterthought. A failure gets logged somewhere nobody looks, and the process simply stops without telling anyone.
A place where items that failed several times are parked, with the error and original data, so a person can fix the cause and replay them instead of losing them.
Yes. We often start there: inventory what is running, add logging and alerts, and build a dashboard before building anything new.
Whatever your team already uses where possible, such as Power BI, a BI tool you own, or the monitoring built into your platform. The numbers matter more than the tool.
The first conversation is free, takes about an hour and covers one process end to end. You keep the map whether or not you hire us.