Every team racing to add AI to a product eventually hits the same uncomfortable question: how much should the model be allowed to do on its own?
It is tempting to wire an agent straight into the parts of your product that move money, change prices, or touch compliance data. That is also how you wake up to an incident report. A large language model is probabilistic by design. It is brilliant at proposing, summarising, and drafting — and fundamentally unsuited to being the final authority on anything that has to be correct every single time.
Propose, validate, dispose
The rule we build into every system is simple: the AI has a voice, not a checkbook. The model proposes an action in natural language. A deterministic, auditable boundary validates that proposal against hard rules — margin clips, entitlement checks, policy gates, human confirmation where the stakes demand it. Only then does the core system dispose: it decides, executes, and records the outcome reproducibly.
The AI is upstream of the decision, never the decision itself.
Why this matters more at volume
The instinct to govern AI gets stronger, not weaker, as a product scales. When agents start reading and writing continuously, the volume of model-initiated actions explodes. A one-in-ten-thousand bad call is a rounding error in a demo and a daily occurrence in production. The boundary is what lets you give the model real reach without betting the business on its worst output.
Give your product a voice. Make its decisions deterministic. The two are not in tension — they are the whole design.