Our AI agent went silent for two days — while the system reported success

Every evening at 9:30 pm, an agent reads the day’s sales from the business management system and writes a summary with margins by product category and recommendations. For two days, that summary never arrived. The automation dashboard calmly reported success.

What happened

The agent runs separately from the system that schedules it. That system connects remotely, starts the job and moves on. The problem was that it counted a successful connection as a successful task — without checking whether the summary had actually been produced. The model’s session had expired. The command failed, returned an error, and that error went no further.

Why this matters to a business owner

A system that fails loudly is frustrating. A system that fails silently is a risk: you keep making decisions as if the information were still arriving.

In a small business, there may be nobody whose job is to watch the automation dashboard.

What changed

Three things, in order of importance:

1. The job now sends an alert when it fails. If the summary is not produced, an alert arrives in the channel where the summary normally appears. The alert does not depend on the component that failed.
2. Success is defined by the result, not the connection. Either the text was produced or the task failed.
3. Missing output is now detectable. Receiving nothing is a signal, rather than an empty space.

The lesson

Before automating a task, decide how you will know if it stops working. If you cannot answer that, the automation is not ready.

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This is one of the questions we bring to a first project: what should happen when the system cannot complete the work?