Building Reliable Agentic AI Systems

2026-06-16 · Source: Martin Fowler · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Advanced, extended

Summary

Bayer AG, in collaboration with Thoughtworks, developed the Preclinical Information Center (PRINCE), a cloud-hosted platform designed to streamline drug development in the pharmaceutical industry. Launched on June 16, 2026, PRINCE utilizes Agentic Retrieval-Augmented Generation (RAG) and Text-to-SQL to integrate decades of safety study reports, evolving from a basic search tool into an intelligent research assistant. The system, built with LangGraph and FastAPI, handles complex queries and drafts regulatory documents by orchestrating specialized agents. Its architecture emphasizes "context engineering" for precise information routing and "harness engineering" for robust orchestration, recovery, and observability. PRINCE prioritizes user trust through transparency, explainability, and human-in-the-loop integration, demonstrating AI's potential to enhance data accessibility and research efficiency while ensuring compliance.

Key takeaway

For MLOps Engineers building agentic AI systems in regulated sectors, prioritize robust "harness engineering" and "context engineering." You should implement state persistence, LLM fallbacks, and multi-stage reflection loops to ensure reliability and recoverability. Displaying intermediate steps and providing granular citations will build user trust and facilitate compliance. Continuously integrate user feedback to refine system performance and maintain data quality through iterative development.

Key insights

Agentic RAG systems require robust context and harness engineering for reliability and trust in regulated environments.

Principles

• Context discipline prevents pollution and aids debugging.
• Multi-stage reflection enhances workflow and data sufficiency.
• Transparency and granular citations build user trust.

Method

The system uses a multi-agent workflow (Clarify Intent, Think & Plan, Researcher, Reflection, Writer) orchestrated by LangGraph. It employs hybrid retrieval (RAG for unstructured, Text-to-SQL for structured) and iterative error recovery.

In practice

• Implement hybrid retrieval (RAG/Text-to-SQL) for diverse data.
• Orchestrate multi-agent workflows with state persistence.
• Display intermediate steps and granular citations for verifiability.

Topics

• Agentic AI Systems
• Retrieval-Augmented Generation
• Text-to-SQL
• Context Engineering
• Harness Engineering
• Pharmaceutical Research

Best for: AI Architect, NLP Engineer, AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Martin Fowler.