Building Agent Studio: How Medable Is Using Agentic AI to Accelerate Clinical Trials
Notas del episodio
Guests
- Luke Bates, Product Leader (Agent Studio), Medable
- Jen Brown, Product Manager, Medable
- Matt Schoolfield, Product Designer, Medable
- Fiachra Matthews, Principal Architect, Medable
What we cover in this episode:
- What Medable does: enabling global clinical trials across 100+ languages and accelerating drug-to-market timelines
- The two agents built on Agent Studio—ETMF (document classification) and CRA (clinical data monitoring)—and the problems they solve
- Why Medable chose a platform approach to agents instead of one-off builds
- How Agent Studio works: models, skills, knowledge bases, MCP connectors, versioning, and trigger types
- Three deployment models: Medable-built products, services-led custom builds, and self-serve platform access
- RAG approaches at scale: embeddings vs. markdown hierarchies vs. just-in-time MCP retrieval
- How they built a unified ontology layer to map terminology across 13 different clinical data systems
- Why they built custom MCPs with an authentication and credentialing wrapper
- Context window management with sub-agents and automatic tool filtering
- Evaluation design in a GXP-regulated environment: golden datasets, production monitoring, and the challenge of human feedback as ground truth
- How they document agent intent → specification → test evidence to satisfy regulatory bodies
- The "full self-driving" vision for clinical trials and what it would take to get there
Resources & Links
- Medable - Clinical trial platform powering Agent Studio
Chapters 00:00 Meet The Medable Team
01:14 Medable Mission And Scope
03:27 Agent Studio Platform Overview
06:29 ETMF Document Automation
08:47 CRA Agent For Monitoring
10:40 Clinical Trial Workflow Primer
14:34 Why Build A Platform
17:51 Learning AI As A Team
21:47 Early Days Of Agent Studio
23:15 How Agents Are Built
25:15 Customer Adoption And UX
30:00 Skills And MCP Standards
31:15 Scaling Context Retrieval
33:07 RAG Patterns And Tradeoffs
34:48 Ontology Data Layer Explained
38:01 Customer Friendly Agent Setup
42:19 MCP Security And Connectors
44:36 Tool Bloat And Subagents
50:44 Evals For Reliable Agents
54:40 Human Feedback Isn’t Truth
57:43 GXP Compliance For Agents
01:03:34 Full Self Driving Trials