What to monitor in a managed AI workflow

A successful connection tells you that two systems exchanged information. The business still needs to know whether the intended work got done. Choose a small set of signals that a manager and an operator can interpret together.

Follow one work item to its destination

Suppose an assistant prepares a morning list of customer orders needing attention. Track how many eligible orders arrived, how many completed checks, how many reached the exception queue and when the last report reached its intended location. If the source silently returns zero rows on a normally busy day, the run may show success while the team receives an empty report.

Add a quality check. Ask a reviewer to sample completed cases and record whether they changed an important field before using the result. Track the type of correction. A drop in runtime means little if staff spend extra minutes repairing incorrect customer matches. Keep review effort and cost alongside technical speed.

Send alerts people can act on

An alert should name the affected workflow, what is delayed, the oldest unfinished item and the person who can respond. For a delay with no immediate consequence, a daily report may do. For a blocked handoff that prevents the next shift from starting, use an alert with a named responder. Google’s reliability guidance similarly distinguishes ongoing monitoring from conditions that merit interrupting a person.

Google’s guide to monitoring distributed systems

Choose thresholds from actual operating hours and the delays your customers or staff can tolerate. Check them again after launch. If a notification routinely produces no action, improve its signal or its destination. The weekly review should make one decision possible, such as narrowing scope, changing a source or addressing the most common correction.

Put this into practice

Bring a sample of the work and the tools your team uses. We can help define a first project, who reviews it, and how to tell if it works.

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