Post-market drug safety monitoring is becoming increasingly complex and data-intensive. This framework presents a sovereign multi-agent system for continuous, real-time pharmacovigilance and adverse event detection that dramatically improves signal detection speed and accuracy.
Executive Summary / Key Takeaways
- Real-time monitoring across multiple data sources
- 50–70% faster adverse event signal detection
- Automated case processing and regulatory reporting
- Reduced manual review workload for safety teams
- Full traceability and explainability for regulatory audits
The Challenge
Delayed signal detection, high manual case processing volume, fragmented data sources, and growing regulatory expectations.
Our Approach / Framework
A multi-agent pharmacovigilance crew with signal detection, causality assessment, automated case processing, and regulatory reporting agents.
Technical Architecture
LangGraph orchestration, multi-source data integration, Ollama medical models, and Qdrant memory on sovereign Swiss infrastructure.
Implementation Guide
12-week implementation with foundation, agent development, validation, and live pilot phases.
Conclusion & Future Outlook
Sovereign Agentic AI enables proactive, continuous drug safety surveillance that protects patients more effectively while reducing operational burden.
