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Agents designed for clinical trials
Agent Studio reimagines the clinical trial lifecycle by giving teams the power to build and deploy agents that automate manual work, remove bottlenecks, and accelerate trial outcomes.
Less friction. More progress.
Medable Agent Studio is a no-code platform for sponsors and CROs to design, deploy, and scale clinical development agents, from ready-to-go solutions to fully bespoke builds.
Connected to your systems
Take the difficulty out of data. Connect seamlessly across 13+ clinical and enterprise systems, eliminate manual stitching, and enable real-time data flow for faster decisions.
Clinical data and regulatory standards
Purpose-built for life sciences. Grounded in GxP, ICH, HIPAA, GDPR, and CDISC to support the rigor required in clinical development.
Human in the loop
You set the guardrails. Define how much control agents have, aligning automation with oversight and your team’s workflows.
Meet Agent Studio
- Quickly configure and deploy agents tailored to your specific clinical development needs
- Maintain human-in-the-loop oversight to ensure transparency and control
- Align with life sciences standards by incorporating SOPs, regulatory and validation requirements, and benchmarking against trusted sources
- Seamlessly integrate with life science systems (eCOA, Veeva EDC, IRT, CTMS) and enterprise platforms
“Every time I see [Agent Studio] I get more excited.”
Eliminate clinical trial white space with the right AI strategy
It has become clear that our industry has reachedthe limits of human-only clinical development. As clinical trials have become increasingly complex, the endeavors that people alone can perform are no longer sufficient to generate the momentum needed to address the growing burden of human disease. This has led to longer drug development timelines and significant delays for patients. One large are of lost time is “white space,” definied simply as unproductive time caused by manual, sequential processes and fragmented data systems. Thankfully, a solution lies in agentic AI and its abilities to perform series of tasks.
From platform to practice, meet your CRA Agent
The CRA Agent unifies data from across clinical systems, automatically surfacing insights and risks so monitors no longer need to log into multiple platforms. By handling routine tasks autonomously and keeping humans in the loop for critical safety oversight, it reduces tech burden, accelerates study timelines, and empowers CRAs to focus on data quality, patient safety, and site relationships.
Frequently asked questions
Agentic AI refers to autonomous, goal-driven AI systems—often called “agents”—that can reason, plan, and act in complex environments with minimal human intervention. In clinical development, these agents can manage and optimize trial workflows such as protocol design, patient recruitment, site coordination, and regulatory documentation. Unlike traditional automation, which follows static rules, agentic AI adapts to new information, learns from trial progress, and proactively orchestrates tasks to keep studies on track and compliant.
AI agents help clinical trials run faster, more efficiently, and with greater quality. By automating repetitive tasks, adapting protocols in real time, and reducing human error, they streamline operations across sites while ensuring compliance and consistency. They also enable more patient-centric approaches by improving communication and engagement, ultimately making it easier to scale complex global trials with fewer resources.
Delivering trustworthy Agent recommendations requires two key elements:
The AI Model – We select and fine-tune models that are purpose-built for life sciences, trained on high-quality, relevant data. This ensures the model understands domain-specific language, context, and regulatory requirements, reducing the risk of inaccurate or irrelevant outputs.
The Agentic Environment & Verification Process – Agents operate in a controlled environment with rigorous validation checkpoints, business logic guardrails, and real-time monitoring. Every recommendation is subject to verification workflows and data quality checks before it’s surfaced to users, ensuring accuracy, compliance, and auditability.
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