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:
increase AuthorizationError(coverage.motive)
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 inner inference cluster as an alternative of a public API endpoint, or blocking the request if no compliant execution setting exists. From the agent’s perspective, the duty nonetheless executes. The orchestration layer ensures it runs in an setting that satisfies enterprise coverage.
Why ontologies are load-bearing infrastructure
For the orchestration layer to make good selections, it must do greater than label knowledge. It wants to grasp how the entities concerned in a request relate to one another, and motive over these relationships to find out what’s allowed.
Take into account the shopper help transcript instance once more. Metadata tells you the dataset comprises PII (personally identifiable data). An ontology lets the system motive throughout a linked chain: the duty operates on a dataset containing private knowledge; that knowledge is ruled by GDPR; the group’s coverage requires processing inside an authorised EU setting; the chosen mannequin runs outdoors that boundary. From these 4 linked information, the orchestration layer can infer the request have to be rerouted or blocked. The system reasoned over the relationships reasonably than matching towards a hardcoded rule tied to a selected dataset.