request = {
"agent": "support-summary-agent",
"task": "summarize",
"dataset": "customer_support_logs",
"model": "external_llm_api",
"delegated_by": "user_4821"
}
policy = evaluate_policy(request)
if policy.allowed:
route_to_execution(policy.execution_environment)
else:
raise AuthorizationError(policy.reason)
The policy engine here evaluates dataset classification, model approval status, geographic processing rules, and the delegation chain that initiated the request. That might mean redirecting the task to an internal inference cluster instead of a public API endpoint, or blocking the request if no compliant execution environment exists. From the agent’s perspective, the task still executes. The orchestration layer ensures it runs in an environment that satisfies enterprise policy.
Why ontologies are load-bearing infrastructure
For the orchestration layer to make good decisions, it needs to do more than label data. It needs to understand how the entities involved in a request relate to each other, and reason over those relationships to determine what’s allowed.
Consider the customer support transcript example again. Metadata tells you the dataset contains PII (personally identifiable information). An ontology lets the system reason across a connected chain: the task operates on a dataset containing personal data; that data is governed by GDPR; the organization’s policy requires processing within an approved EU environment; the selected model runs outside that boundary. From those four connected facts, the orchestration layer can infer the request must be rerouted or blocked. The system reasoned over the relationships rather than matching against a hardcoded rule tied to a specific dataset.
