Scaling Agents in Healthcare & Life Sciences: Lessons from Madrigal Pharmaceuticals, Abridge, and Vizient
LangChain published an account of how three healthcare and life sciences organizations are building agent systems, describing constraints it says differ from most industries.
The report states that in healthcare and life sciences, earning the trust required to scale agents is harder, and that trust functions as an audit requirement, a compliance obligation, and in some cases a patient-safety requirement. It says meeting that bar requires an infrastructure layer many teams may not have built into first pilots.
Among patterns cited: 76% of healthcare and life sciences organizations LangChain speaks with name tracing, evaluation, and spend visibility as requirements before agents are given more autonomy; 43% are focused on PHI handling, de-identification, and HIPAA requirements; 49% are working on a company-wide agent platform, control plane, or "agent factory" as their primary use case; 33% are building agents for workflows with an existing paper trail and known cost per case; and 26% are each running or building external-facing agents and letting non-engineers build agents within central guardrails.
Examples described in the report:
Madrigal Pharmaceuticals, a biopharmaceutical company focused on metabolic dysfunction-associated steatohepatitis (MASH), built an enterprise multi-agent platform that helps employees search, analyze, and synthesize evidence across structured systems, documents, and external sources, using an orchestrator and modular skills, with observability via LangSmith.
Abridge builds AI that turns clinician-patient conversations into clinical documentation and supports a persistent agent across the clinical workflow, and now works with more than 250 health system partners across more than 50 specialties and 28 languages, recording more than 100 million conversations a year. The report cites a hill-climbing effort on its History of Present Illness and Problem-Based Assessment and Plan models producing a 17% gain in accuracy and a 19% gain in completeness, with release cycles reduced from one to two months to days.
Vizient built a GenAI platform that lets healthcare providers query siloed hospital data, using a hierarchical structure with worker agents reporting to a supervisor agent.
Based on reporting from the original publisher. Visit the source for full context and later updates.
Publisher excerpt
Agent programs in healthcare and life sciences are being built under a different set of constraints than those in most industries. There’s plenty of upside if the constraints can be resolved. Success can mean hours of manual review compressed into minutes, data spread across a dozen systems finally queryable in one place, and clinicians getting time back from documentation. At the same time, the cost of a wrong answer can be higher here than almost anywhere else, which changes how teams build.