Use cases › Let a morning agent water the garden within hard limits
Let a morning agent water the garden within hard limits
A gardener instrumented a 2 m² vegetable bed with soil probes, a weather station and a camera, then scheduled a morning agent to read the history and the forecast and adjust watering. A fixed rules engine kept being wrong when context mattered. The agent may make at most one change per zone per run, keep minutes between 1 and 15, and must log what it expects so it can check the next day. An old Android phone taps the official Rain Bird app to apply the change. As of 5 November 2026 the garden had a little over four million readings.
Prompt you can copy
A generic template written by Milo for this use case, not the original user's prompt.
View prompt
Every morning, review yesterday's [SENSOR READINGS], the photos, and the [WEATHER FORECAST]. Decide whether to change watering for each zone. Hard limits: at most one change per zone per run; change duration by at most 20%; keep minutes between 1 and 15; never turn watering off unless at least 5 mm of rain is forecast in the next 24 hours. Write down what you changed and what you expect to see in the soil tomorrow, then check that expectation on the next run. If you are unsure, do nothing and tell me why.
A generic template written by Milo for this use case, not the original user's prompt. Swap the [placeholders] before you run it.
Get 5 agent use cases every morning
Each one with a prompt you can copy. Plus the Weekly AI tools ranking every Friday. Free.