back_office_ops · healthcare · workflow
Madrigal Pharmaceuticals builds a pharmaceutical-grade multi-agent AI research platform with LangChain and LangSmith
Madrigal's enterprise data was fragmented across structured systems, unstructured documents, external sources, and real-time APIs with no unified way to search, analyze, and synthesize information at scale; each source behaved differently with incompatible formats, access patterns, and expectations.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Employee submits query
Madrigal employees search, analyze, and synthesize relevant data spread across the enterprise within appropriate access controls.
Tools used
LangChainLangGraphLangSmithDeepAgentsGitHub
Outcome
Madrigal's multi-agent platform enables new use cases to go from weeks of development to hours, with prototype-to-enterprise deployment taking weeks rather than months, and the system scales to new domains without rewriting orchestration logic or adding new infrastructure.
Results
Time savedweeks is reduced to hours
Grounding & classification
Source type: vendor customer story
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agentic workflowenterprise searchmulti agent workflowragsummarizationknowledge basebuilder submittedmetric backednamed customerproduction runtime claimedtools describedworkflow describedpharma life sciencescycle time reductionemployee productivityvendor customer storyback office opsagentic task execution