What are Policy Validators?

While it's appropriate to steer some behavior through natural language, enforcing business rules through natural language can be unreliable.

Natural language policies are useful when there is a lot of nuance, but they are too imprecise for strict rules.

Example	Optimal Steering Behavior
"If the escalation seems important, surface it to the on-call engineer"	Natural Language Let the LLM decide if it's important.
"If the employee is not an FTE, don't show them our FTE benefits"	Rules ` employee.status IN ["Contractor", "Contingent"]`

Example

Optimal Steering Behavior

"If the escalation seems important, surface it to the on-call engineer"

Natural Language

Let the LLM decide if it's important.

"If the employee is not an FTE, don't show them our FTE benefits"

Rules `

employee.status IN ["Contractor", "Contingent"]`

Natural language policies can be misunderstood by LLMs, or users might jail break them.
The more policies you add, the less likely your AI agent will reliably enforce policies (Lost in the Middle problem).

Policy Validators enforce & guarantee that rules are always respected accurately — no matter how many you add.

How do you configure Policy Validators?

We have policies embedded in different parts of our product to provide you control where you need it

Activity Confirmation Policies - enforces user approval before an action in taken.
Slot Validation Policies - validates input values satisfy constraints
Slot Inference Policies - decides if the LLM must ask the user for an input.
Conversational Process Decision Policies - controls agentic decision-making with rules