Need Expert Review, Testing & Fixes for Agentic RAG 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. The application requires urgent testing and bug fixing. I need it to be fully reviewed and addressed by 23/06/2026. The application requires urgent testing and bug fixing, and I need it completed by 23/06/2026. I would appreciate it if you could review the architecture, identify any potential issues or improvements, and provide your feedback as soon as possible. Your response would be greatly appreciated due to the tight deadline. Salma B.