QWEN Cloud Hackathon - #1 Technical Deep dive on what I am building

A developer built Smriti, a 4-agent elderly care system using QwenVL, MemoAssistant, and persevere thinking to address dementia patient needs. The system includes a Vision Agent for recognition and hazard detection, a Memory Agent for persistent storage, a Guardrail Agent for hallucination blocking, and a Caregiver Agent for human notification. The agents communicate in under 2 seconds to provide real-time monitoring and alerts.

The Problem: A week ago I went to my grandmother who is suffering from demantia. At first I thought it was normal at this second but i wanted to know the cause so i looked up in google and saw that over 55 million patentis are navigating a world where they wake up and cannot recognize their own children's faces. They miss critical life spanning medications and suffer catastrophic falls when unmonitored. We cannot solve a dynamic, high stakes human crisis with a single, static LLM prompt or a standard chatbot wrapper. If a healthcare AI hallucinates a medication dosage or fails to recognize a family caregiver, the consequences are life threatening. That's why we are introducing Smriti - Building a 4-Agent Elderly Care system with QwenVL, MemoAssistant and persevere thinking. The Solution - 4 Specialized QwenNative Agents working together: Agent1: - Vision Agent Qwen-VL - Responsible For Recognition, medicine label reading, hazard detection.It is optimized to perform high speed facial detection, medicine label reading, and spatial hazard analysis such as identifying a water spill on the floor or an open stove burner . Agent2: - Memory Agent MemoAssistant : Persistent KeyValue Storage Across sessions.The Memory Agent uses MemoAssistant to manage persistent key-value storage across sessions. It caches family profiles, authorized medical staff, and historical routines so the system doesn't have to re-evaluate static profiles on every frame loop. Agent3: - Guardial Agent Qwen 3.6 Max + Persevere Thinking - Hallunication blocking with reasoning traces.It utilizes Qwen's trillion-parameter MoE architecture and forces a reasoning trace calculation using the preserve thinking parameter. This layer acts as a strict hallucination blocker by forcing the model to explicitly evaluate safety weights and confidence before executing actions. Internal Reasoning for Guardrail Agent: Agent4: - When the Guardrail Agent detects an anomaly or drops below the 85% confidence score, it forwards the state payload to the Caregiver Agent via the Model Context Protocol MCP . This pushes live notifications, image clips, and the system's reasoning logs directly to a web-based dashboard for immediate human approval. How Agents Communicate - Vision - Memory - Guardrail - Response in under 2 seconds. One of the reason to build this - Only in reddit there are like 70K poeple in the subreddit channel of dementia.