request = {
"agent": "support-summary-agent",
"activity": "summarize",
"dataset": "customer_support_logs",
"mannequin": "external_llm_api",
"delegated_by": "user_4821"
}
coverage = evaluate_policy(request)
if coverage.allowed:
route_to_execution(coverage.execution_environment)
else:
elevate AuthorizationError(coverage.purpose)
The coverage engine right here evaluates dataset classification, mannequin approval standing, geographic processing guidelines, and the delegation chain that initiated the request. That may imply redirecting the duty to an inside inference cluster as an alternative of a public API endpoint, or blocking the request if no compliant execution atmosphere exists. From the agent’s perspective, the duty nonetheless executes. The orchestration layer ensures it runs in an atmosphere that satisfies enterprise coverage.
Why ontologies are load-bearing infrastructure
For the orchestration layer to make good choices, it must do greater than label knowledge. It wants to grasp how the entities concerned in a request relate to one another, and purpose over these relationships to find out what’s allowed.
Contemplate the shopper assist transcript instance once more. Metadata tells you the dataset accommodates PII (personally identifiable info). An ontology lets the system purpose throughout a related chain: the duty operates on a dataset containing private knowledge; that knowledge is ruled by GDPR; the group’s coverage requires processing inside an authorized EU atmosphere; the chosen mannequin runs exterior that boundary. From these 4 related info, the orchestration layer can infer the request should be rerouted or blocked. The system reasoned over the relationships relatively than matching in opposition to a hardcoded rule tied to a selected dataset.
