This design balances performance, cost, and capability constraints. Standard NLP (A) provides fast, low-cost, deterministic routing for simple cases, meeting the 300ms SLA and cost ceiling. Azure CLU (D) handles more complex multilingual intent recognition efficiently without external data stores. Generative orchestration (F) is reserved for high-value, complex tasks like drafting replies and managing approval-gated tool workflows, where its advanced reasoning is necessary. This hybrid approach is maintainable and cost-effective.

B. Generative orchestration for all routing: This would be too slow and expensive for high-volume triage, violating the SLA and cost ceiling.

C. External vector store for routing signals: This is explicitly forbidden by the requirement to avoid new external data stores for PII.

E. Multi-agent runtime with separate audit lake: A separate audit lake constitutes a new external data store, which is not permitted.

1. Microsoft Copilot Studio Documentation, "Use generative actions in Microsoft Copilot Studio," Section: Overview of generative actions. This document distinguishes between traditional intent-based topics (like CLU) and dynamic, generative orchestration for complex tasks.

2. Microsoft Azure AI Language Documentation, "Conversational language understanding (CLU) overview," Section: How CLU works. This explains how CLU is used for robust intent classification and entity extraction, suitable for performant, multilingual routing within the Azure ecosystem.

3. Microsoft Power Platform Documentation, "Governance and security in Microsoft Copilot Studio," Section: Data loss prevention (DLP) policies. This guide discusses keeping data within tenant boundaries, a key constraint met by using internal services like CLU over external stores.