Conceptual Guide Scaling Agents in Healthcare & Life Sciences: Lessons

Conceptual Guide Scaling Agents in Healthcare & Life Sciences: Lessons from Madrigal Pharmaceuticals, Abridge, and Vizient Jess Ou September 14, 2026 13 min Go back to blog Create agents Share 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. Across payers, providers, and biopharma, we are seeing that earning the level of trust required to scale agents is much harder. In this industry, trust goes beyond product quality and is also an audit requirement, a compliance obligation, and in some cases a patient-safety requirement. Meeting that bar requires an infrastructure layer that many teams may not have built into their first pilots. This piece looks at how three organizations are building their agents: Madrigal Pharmaceuticals built an enterprise multi-agent platform that helps employees search, analyze, and synthesize evidence across structured systems, documents, and external sources. Abridge builds AI that turns clinician-patient conversations into clinical documentation and supports a persistent agent across the clinical workflow. Vizient built a GenAI platform that lets healthcare providers query siloed hospital data to answer questions such as whether ambulatory investments are paying off or where care can be delivered more cost-effectively. Alongside these three, we’ll draw on patterns we’re seeing emerging across a broader set of healthcare and life sciences agent programs. The emerging patterns in healthcare and life sciences agent programs Observability, evals, and cost control are becoming prerequisites for greater agent autonomy. This is the most common theme we hear. 76% of healthcare and life sciences organizations we speak with name tracing, evaluation, and spend visibility as requirements before agents are given more autonomy. In regulated settings, the need extends beyond debugging to evidence. Teams need a durable record of what an agent did, who reviewed it, and how quality was measured because that is the record a compliance function will eventually ask for. For health plans and providers, 43% of the organizations we speak with are focused on PHI handling, de-identification, and HIPAA requirements . Several teams are also putting an LLM gateway in front of their models to gain unified visibility into spend across users and models before expanding agent autonomy further. Central agent platforms are consolidating fragmented agents across the enterprise. 49% of organizations we speak with are working on a company-wide agent platform, control plane, or “agent factory” as their primary use case, with business-unit agents running on top of it. We see this pattern across pharma, payers, and health systems. A dozen or more teams each build their own agent, each recreates the same foundations, and eventually one team is asked to own the shared layer. The shared layer can take the form of a reusable template library, an internal agent catalog, or a single governed path from prototype to production. One top-five pharma company is consolidating hundreds of applications onto a single interoperable platform. One payer we spoke to recently went from a single production agent to scoping roughly a hundred more on a unified foundation. For many organizations, hundreds of proofs of concept without a clear path to production are often the starting point. Regulated document and back-office work is seeing the clearest ROI. 33% of organizations we speak with are building agents for workflows that already have a paper trail and a known cost per case. These include: Clinical study reports and regulatory submissions FDA correspondence extraction Medical-legal review GxP document generation Protocol OCR Prior authorization Claims rework and coordination of benefits Purchase-order and invoice ingestion These use cases carry a clear before-and-after metric, and several agents are already running in production. Work that might take a medical writer or processing team hours can now be measured in minutes, while filing timelines themselves become metrics that leadership can track. Patient- and member-facing conversational agents are moving from pilot to production, including voice. 26% of organizations we speak with are running or building external-facing agents across member navigation, patient intake and triage over SMS and WhatsApp, consumer device assistants, and contact-center deflection. Voice has become a meaningful part of these programs, with teams tracing and scoring audio interactions for sentiment, adverse-event mentions, and PII exposure. Safety evaluation is tightly connected to this use case. Mental-health providers, for example, are explicitly testing for off-track conversations and suicidal-ideation detection before scaling deployment. Federated building initiatives often emerge when central engineering becomes a bottleneck. 26% of organizations are trying to let non-engineers build agents within central guardrails. We’re seeing business teams configure and validate agents against internal sources such as SharePoint, EHR summaries, or Snowflake, while a central team industrializes the ones that are proving valuable. We’re also starting to see subject-matter experts own prompts and evaluation datasets directly. Clinicians and pharmacists can edit prompts and trigger evals in development, with engineering promoting the versions that pass. Scientific R&D agents are longer-running. Scientific R&D organizations are creating agents for discovery and lab science, including target discovery, structure-based design, omics and single-cell perturbation analysis, literature and knowledge-graph retrieval, and lab-instrument control. These are also among the longest-running and least deterministic agents in the industry. As a result, these teams place especially high demands on evaluating the full trajectory an agent takes, rather than judging only its final answer. Clinician and care-team support use cases are prevalent with providers and payers. Many organizations are building agents that work alongside clinicians or care managers, including pre-visit preparation, care-navigation orchestration, chart preparation, and referral management. Human-in-the-loop is generally assumed for these workflows. The larger blockers tend to be EHR integration and audit obligations. The three organizations below show what it takes to operate agents once a company has moved beyond its first pilot. Three teams building agents in production Madrigal Pharmaceuticals: One platform with many skills Madrigal Pharmaceuticals is a biopharmaceutical company focused on metabolic dysfunction-associated steatohepatitis (MASH), a serious form of fatty liver disease. Its enterprise agent platform grew out of the challenge of integrating, searching, and synthesizing information scattered across structured systems, unstructured documents, external sources, and real-time APIs. The first major constraint was that every data source behaved differently, with different formats, access patterns, and expectations. Madrigal normalized those sources into the same secure data warehouse and exposed them through a single, consistent tool interface. From an agent’s perspective, all information became available through the same abstraction, allowing the system to add new domains without rewriting orchestration logic each time. This abstraction helped the team turn one workflow into a broader platform. An orchestrator built with LangChain’s Deep Agents harness receives a task and determines which capabilities are needed, which agents should run, and what work can happen in parallel before the results are reconciled. Its role is to route the problem across specialized capabilities rather than encode the details of every domain. New use cases are added as modular skills that define how to approach a particular type of problem and what good output looks like. This approach to using skills brought new use case development down from weeks to hours. Parallelism helps the system handle more complex research efficiently. A research question can be divided across sub-agents, each handling a different slice of the problem, while those sub-agents can further parallelize their own work. A shared virtual filesystem built into the Deep Agents harness acts as the system’s memory. Results, sources, and intermediate steps are written down and made available for reuse, simplifying coordination as the system scales. For observability, Madrigal leaned on LangSmith to gain full pipeline visibility into every tool call, retrieved chunk, and agent decision. As Parth P