We treat LLM output as untrusted input. Always. Our system introduces two layers:
1. Chat (generation layer)
- →define workflows using natural language
- →generate structured steps
- →integrate across systems
2. Control (validation layer)
Before any execution:
- →Every step is validated
- →Every value is checked
- →Every constraint is enforced
Flow becomes: Chat → structured workflow Control → validation + constraints Execution → safe, bounded actions
The model proposes. The system decides.
Impact on Cost and Quality
Most teams optimize for fewer tokens and faster responses. But real optimization is: reducing cost of incorrect execution.
With control in place, cost reduces because:
- →fewer retries
- →fewer manual corrections
- →fewer failed workflows
- →less operational overhead
Quality improves because:
- →Only valid outputs execute
- →state remains consistent
- →workflows behave predictably
Cause → Effect Validation layer → fewer errors → lower operational cost → higher system trust
This is proportional: As validation increases, the cost of failure decreases and the quality of execution increases.
Enterprise AI|LLM Controls|AI Architecture|AI Governance