AI in production, bounded by rules it cannot break.
A general-purpose model will happily generate an aggregate that breaks the rules, or act in ways the business forbids. A domain model has rules a framework can check and a boundary it enforces, so you can put AI in production and keep control of what it is able to touch.
Generation, then verification
An AI proposes a model, Protean validates it at startup and says exactly what is wrong, and the AI corrects what the framework rejects. You can let a model generate and still trust the result, because an invalid model fails before it ever runs.
The command boundary is the guardrail
Every state change goes through a command that must satisfy the aggregate's invariants. Put an AI agent behind that boundary and it cannot do anything your domain rules forbid. The framework enforces the rule in code on every change, so the guardrail holds whatever the AI is prompted to do.
Auditable by design
Turn on event sourcing for the aggregates that matter and every change becomes a recorded fact. What an AI decided, and when, is auditable and replayable, so you can show exactly what it did before you let it act on anything that matters.
A safe seam to the systems you already run
A subscriber consumes events from an existing system and translates them into the clean model, an anti-corruption layer at the boundary. You can put AI beside a legacy system and modernize it one piece at a time, with the core kept intact and no big-bang rewrite.