Need Expert Review, Testing & Fixes for Agentic Healthcare Application

I'm working on ClinixAI, a multi-agent healthcare assistant platform. The system includes: A Patient Agent that handles appointment booking, doctor search, pre-consultation questionnaires, and patient support. A Doctor Agent that assists healthcare professionals by managing appointments, accessing patient information, and generating consultation summaries. A hybrid memory architecture with: Redis for short-term conversational state. MongoDB for long-term patient profiles and behavioral memories. A memory RAG system that learns patient preferences (preferred doctors, specialties, language, appointment times, etc.) and injects relevant memories into the LLM prompt. The architecture is microservice-based. Agents do not access databases directly; they communicate through backend services. The RAG pipeline has two phases: Memory indexing: extract behavioral signals → generate embeddings → store in MongoDB. Memory retrieval: semantic search over user memories → hybrid ranking → inject memory context into the LLM prompt. Current focus: improving personalization, doctor preference ranking, multilingual intent extraction, and memory-based retrieval. I'd appreciate your help reviewing the architecture and identifying potential improvements.