Search policy entries

GET

/api/workspaces/{workspaceId}/policy-entries/search

• Production

Performs a full-text search across policy entries in a workspace. Searches against topic and rule text. An empty query returns no results.

Authorizations

• bearerAuth

Parameters

Path Parameters

workspaceId

required

string format: uuid

The workspace to search within.

Query Parameters

q

string

Search query to match against policy content.

Example

discount

Responses

200

Policy entries matching the search query.

List of policy entries.

Array<object>

A policy entry defines a rule that governs how the AI should behave in specific situations. Unlike KB entries (which provide factual answers), policies specify constraints, guardrails, and behavioral guidelines. For example, ‘Never offer more than 10% discount without manager approval’ or ‘Always ask for an order number before looking up shipping status’. Policies can be created manually or proposed by the learning loop.

object

id

required

Unique identifier for the policy entry.

string format: uuid

Example

pe1a2b3c-5678-9abc-def0-1234567890ab

tenantId

required

The tenant this policy belongs to.

string format: uuid

Example

a0b1c2d3-4567-89ab-cdef-0123456789ab

workspaceId

required

The workspace this policy is scoped to.

string format: uuid

Example

w1a2b3c4-5678-9abc-def0-1234567890ab

topic

required

The topic or domain this policy applies to. Used by the AI to determine which policies are relevant to the current conversation.

string

Example

discount_authorization

ruleText

required

The policy rule in natural language. This is the instruction the AI follows.

string

Example

Never offer a discount above 10% without creating an attention item for manager approval. For discounts up to 10%, apply automatically if the customer has placed 3 or more orders.

conditions

Optional structured conditions that qualify when this policy applies. Can include time-based, value-based, or context-based conditions.

object | null

Example

{
  "min_order_value": 500,
  "customer_tier": "gold"
}

sourceType

required

How this policy was created. ‘manual’ means created by a human, ‘learning_loop’ means proposed by the AI after analyzing resolved conversations.

string

Example

manual

confidence

Confidence score between 0 and 1, set by the learning loop when proposing policies. Null for manually created policies.

number | null

Example

0.85

status

required

Lifecycle status. Same semantics as KB entry status — only ‘approved’ policies are enforced by the AI.

string

Allowed values: pending_review approved rejected archived

Example

approved

createdAt

required

Timestamp when the policy was created.

string format: date-time

Example

2026-02-05T09:00:00.000Z

updatedAt

required

Timestamp when the policy was last updated.

string format: date-time

Example

2026-03-18T11:30:00.000Z