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Building Reliable Agentic AI Systems

A case study on building a production-ready agentic AI system using Retrieval-Augmented Generation (RAG) and multi-agent workflows.

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Building Reliable Agentic AI Systems

martinfowler.com
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The resource presents the Preclinical Information Center (PRINCE), an agentic AI system developed by Bayer AG with Thoughtworks to address pharmaceutical industry challenges in drug development.

PRINCE leverages RAG to integrate decades of safety study reports, enabling researchers to pose complex questions and receive accurate answers. The system prioritizes trust through transparency, explainability, and human-in-the-loop integration.

Abstract

The resource presents the Preclinical Information Center (PRINCE), an agentic AI system developed by Bayer AG with Thoughtworks to address pharmaceutical industry challenges in drug development. PRINCE leverages RAG to integrate decades of safety study reports, enabling researchers to pose complex questions and receive accurate answers. The system prioritizes trust through transparency, explainability, and human-in-the-loop integration.

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agentic ai systemspreclinical data retrievalgenerative airetrieval-augmented generationthoughtworksbayer agAI AgentsRetrieval & RAGLarge Language ModelsContent Engineering
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