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A pattern I use in AI-assisted operations: define the task and its boundaries, let the tool draft or analyze, require human review before consequential changes, then verify the result and document what happened.
This helps with Linux, Docker, and API workflows too. Automation is most useful when the next person can understand and maintain it.
Where do you put the human approval step in your own workflows?
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The brief-writing piece is interesting — curious how you handle the validation step. AI-generated language tends to sound confident even when the underlying data is sparse or the trend is ambiguous. One pattern that helps: have the model output a confidence signal alongside the...
One thing I didn’t expect when building AI automations:
The hardest part isn’t getting the LLM to work. It’s handling everything around it.
A workflow can fail even when the model gives a good answer.
What if:
The LLM returns incomplete data?
An API times out?
The extracted information doesn't match the expected format?
That’s why I’ve started thinking about AI automation as a system, not just an LLM call.
The model is only one component.
Validation, retries, fallbacks, logging, and deterministic logic matter just as much.
A working AI demo isn't necessarily a reliable AI system.
What’s been the biggest reliability challenge in the AI workflows you’ve built?
One useful test is an API timeout after the receiving system has already created the record. A retry can look successful while quietly creating a duplicate.
I would separate read-only retries from actions that send, book or write. Give each intended action a stable identifier;...