Oogway Labs engineering
From the blog
What we learn shipping AI systems that have to keep working after the demo.
An agent needs a stop condition more than a better model
Agents rarely fail in production by reasoning badly. They fail by looping, by answering confidently when they should stop, and by having nowhere to hand the problem.
Most AI cost lives in the pipeline, not the model
Swapping the model is the first thing teams try and the smallest lever they hold; the order you do the work in decides how much of the saving you actually keep.
The eval set is the spec. Build it before the agent.
A labelled evaluation dataset is what turns “it seems better” into a number, and writing it first changes the order of the entire engagement.
Sometimes the right AI decision is to ship no agent
On a radiology auditing platform the constraint was routing and visibility, not reasoning, so the first release contained no agent at all, and that is why it shipped.
The enterprise IT security review is a sales stage. Answer it first.
A questionnaire you answer in week one costs a document exchange; the same questionnaire in month four costs a re-architecture and a quarter of runway.
A ten-minute call is a context problem, not a speech problem
Transcription accuracy is the easy half of voice AI; holding ten minutes of a conversation in usable form, and handing it to a person intact, is the half that decides whether callers trust it.
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