Chat interfaces make it look like you're building systems. You're not. You're generating instructions.
Prompt: "Extract invoice → validate → update CRM"
What you get:
- →a sequence of steps
- →loosely structured output
- →no guarantees of correctness
Start → Process → End Chat → LLM generates workflow → system executes That middle layer is missing.
In production, this breaks fast:
- →invoice extracted with the wrong vendor ID
- →validation step skipped due to ambiguity
- →CRM updated with incorrect mapping
Nothing crashes. But your system state is now wrong.
The Core Mistake
Treating generated workflows as executable workflows. They are not the same.
LLMs are good at: → proposing structure They are not reliable at: → enforcing correctness
So the system needs a boundary.
Correct Architecture
Chat → generates workflow Control layer → validates every step System → executes only what passes
Where validation includes:
- →schema enforcement
- →semantic checks (IDs, values, references)
- →step ordering constraints
- →rejection of invalid states
Now the model can be wrong. The system won't be.
This is what we're building at llmcontrols.ai: A layer where workflows can be generated via chat, but execution is always controlled, validated, and bounded.
Take one workflow in your system. Write down: Generated steps → ? → Execution If that "?" is not explicitly defined, your system is executing assumptions.