AgentGuard

Agent Guard is a real-time safety checkpoint designed to evaluate AI-generated clinical recommendations before they are presented for human review. Rather than replacing clinicians or medical AI systems, it acts as an independent verification layer that analyzes recommendations in the context of patient information and configurable safety policies. The application consists of a FastAPI backend and a Streamlit frontend. Users can enter patient demographics, symptoms, medical conditions, medications, allergies, vital signs, laboratory values, and a clinical question. The system can generate and verify a recommendation or verify an existing recommendation through the same workflow. The verification pipeline evaluates recommendations using modular safety checks and policy-based rules. Based on the collected evidence, the decision engine returns either PASS or FLAG together with a risk level, confidence score, explanation, and detailed verification results. Every verification is stored in an audit log to improve transparency and traceability. Clinical policies are stored in YAML files and can be reloaded without changing application code, allowing safety rules to be updated independently from the software itself. Audit records can also be viewed through the API. The project is fully containerized using Docker and exposes REST APIs through FastAPI while providing an interactive user interface with Streamlit. The architecture was designed to be modular so additional verification modules, policies, or language models can be integrated with minimal changes. The current prototype demonstrates how an independent governance layer can increase transparency and provide structured safety checks for AI-assisted clinical decision support workflows.