An AI assistant can give different answers for the same inputs. An application written by an AI assistant, if it doesn't just call an LLM at runtime, will mostly likely produce the same answer for the same inputs. That is, unless there's something intrinsic to its business logic that makes it nondeterministic.
Sometimes you really want the latter even if the former is less effort.
A *human* assistant can give different answers for the same inputs. At some point, we'll need to stop treating computers as instruction executors and start treating them as autonomous agents. Non-determinism isn't a bug to be fixed, it's just the reality of working with this new kind of machines, and operators of those machines need to embrace that fact, like they did for human assistants.
I want an agent studio and execution sandbox where my LLM driven assistant builds and maintains my apps with a receipt or transcript like history of what it did to create each app. Determinism built per use case with non deterministic assistance orchestrating the build and management layer. Extra credit if I can trivially share and collaborate with others per “deterministic silo” via a shared link. Something like Claude Code + AWS Lambda|(Docker|Podman) + Tangled.org with LLM code generation for my phone and workstation.
The LLMs empower for building, the determinism improves output-expectation alignment. It’s fundamentally an on device software factory, hypervisor, and storage system.
And when applicable, the program would be much more efficient than the LLM.
So it would really be beneficial for the LLMs to be able to have their own environments to write and run code, but also to store those programs for later use automatically.