One frustrating thing about working with AI is that it breaks quietly.
A model gets updated. A default changes. Some layer on top of the model changes behavior. If you use tools like OpenClaw or other wrappers, what used to work can suddenly stop working — and you don’t always know why.
That uncertainty is one of the hidden costs of building with AI.
It’s also why I now separate my recurring tasks into two buckets.
1) Tasks that should not change
If a task is repetitive and predictable, I don’t want AI improvising every time. I want a cron job or a standard script.
A good example is checking a portfolio.
What I actually want is simple:
- calculate total net asset change
- show individual stock movements
- highlight anything that needs attention
That should be a fixed workflow.
AI can help me write the script, improve the logic, or clean up the formatting. But once the task is defined, I want the system itself to stay repetitive and predictable.
Then the outcome becomes clear:
- it ran, or
- it failed
That’s the kind of reliability I want for recurring tasks.
2) Tasks that still need AI reasoning
Some recurring tasks still need judgment — reading the web, summarizing sentiment, spotting changes, and so on.
For those, I try to force as much structure as possible:
- use a standard output format
- tell the AI not to hallucinate
- make missing data explicit
- if access or auth breaks, alert immediately
That last one matters a lot. Silent failure is worse than loud failure.
The trade-off
- Upside: AI gives flexibility where fixed rules are not enough
- Downside: model and platform changes can quietly reduce reliability
My current lesson: use AI where reasoning helps, but use fixed code where consistency matters.
The more important the task, the less “magic” I want in the workflow.