Building Agentic Workflows: A Practical Field Guide for MSLs
Discover how agentic AI can transform your field intelligence workflow. Learn to build specialized agents that extract, evaluate, and categorize medical insights automatically, freeing you to focus on what matters most: scientific engagement and strategic impact.
Agentic workflows for field medical intelligence
Every day, Medical Science Liaisons (MSLs) capture a wealth of field intelligence: insights that shape strategy, identify unmet needs, and guide scientific engagement. Yet most of that intelligence remains buried in unstructured notes, requiring hours of manual review. By some estimates, MSLs spend 6–8 hours each week summarizing, categorizing, and synthesizing field data. This represents valuable time that could be spent advancing scientific engagement.
Agentic AI changes that equation. By planning, executing, and refining data workflows, agentic systems can automatically extract, evaluate, and categorize medical insights from raw CRM inputs. This agentic workflow frees MSLs to focus on higher-level engagement.
Build, measure, and connect agents
This guide provides a practical roadmap for designing and implementing agentic workflows within Medical Affairs. You'll learn how to:
Build and test four core agents — Parser, Evaluator, Categorization, and Insight Analysis — that automate field intelligence
Measure performance with precision, recall, and F-1 scores
Connect agents into an orchestrated workflow that scales insight production while maintaining compliance
Whether you’re experimenting with a single agent or architecting an enterprise-grade orchestration layer, this guide offers the principles, templates, and prompts to move from theory to implementation.
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