AI CONSULTING & AUTOMATION / A PRACTICAL ANSWER
How should you monitor errors after an AI workflow launches?
The short answer
Monitor an AI workflow by defining which failures matter, how they are detected and who can act on them. A system can keep running while its outputs become less useful. Include quality review and source changes alongside technical availability, with information handling appropriate to the data involved.
What to establish
Record the expected task, evaluation criteria and escalation thresholds. Use suitable samples or checks to identify unsupported outputs, missed information and unusual inputs. Avoid collecting sensitive material simply because it might be convenient for debugging.
How to assess readiness
Review incidents for their operating cause: source quality, changed inputs, integration behaviour, instructions or model/provider changes. Keep a change record and retest relevant cases after a correction. Define when the workflow must pause instead of continuing with uncertain results.
ILLUSTRATIVE EXAMPLE / NOT A CLIENT RESULT
Make it concrete.
An assistant may begin missing a field after a source document format changes. A server-health dashboard could remain green. Monitoring the task outcome and input assumptions is therefore a separate responsibility from checking that requests are being processed.
Before you commission
- Define meaningful failure signals and owners.
- Use proportionate records and quality checks.
- Retest changes and maintain a working pause route.
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