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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.
The latest from Knowledge Center
Playing catch-up: FDA wants “patient’s voice” ePRO in your oncology trial
For years now, the FDA has been making one point crystal clear to sponsors and CROs across our industry; they want the patient voice incorporated whenever possible in oncology trials.
The FDA's initiative is driven by the recognition that a patient's personal experience with a disease and its treatment is a unique and essential measure of a medical product's benefit and risk.
- Rationale: The FDA explicitly states that "patients provide a unique perspective on treatment effectiveness" and "some treatment effects are known only to the patient." Outcomes that truly matter to patients, such as functioning, quality of life, and the burden of side effects, are often best measured directly by the patient.
- Mandate: The Patient-Focused Drug Development (PFDD) effort, codified in part by the 21st Century Cures Act, requires the inclusion of such patient experience data in clinical research.
- Guidance series: To formalize this approach, the FDA has released a series of methodological guidance documents (the PFDD Guidance Series) that outline how stakeholders should collect, submit, and use patient input to inform medical product development.
Build vs buy: A guide on adopting AI agents for life sciences
“Big corporations can’t rely on their internal speed to match the transformation that is happening in the world. As soon as I know a competitor has decided to build something itself, I know it has lost.”
These candid sentences from Sanofi CEO, showcase one of the most common questions that’s at the forefront of every pharmaceutical company’s mind; whether to build or buy your way into the agentic and generative AI revolutions.
In life sciences, many teams start with the same instinct. They see a capable large language model, stand up a proof of concept, and feel close to a breakthrough. For most of us, AI prototypes can look magical. A chatbot summarizes visit reports, drafts emails, or answers protocol questions in minutes. The experience is so strong that teams assume production is a short step away.
Unfortunately, the gap is much bigger than it looks.
According to a recent MIT study, 95% of AI pilots will fail, as they note that “Only 5% of custom GenAI tools survive the pilot-to-production cliff, while generic chatbots hit 83% adoption for trivial tasks but stall the moment workflows demand context and customization.”
Like MIT’s example shows, moving from prototype to production in clinical research means building something validated, compliant, scalable, and integrated into real workflows. That takes far more than clever prompts. It requires domain grounding, continuous monitoring, retraining loops, robust tool orchestration, and evidence that the system is safe and auditable under regulations like GxP, HIPAA, and 21 CFR Part 11.
Many organizations only discover the hidden costs after they have committed. Internal teams often invest for two years, spend millions in sunk cost, and still never reach a dependable clinical grade system. The illusion comes from how easy it is to get an early demo working, and how hard it is to make that demo survive contact with trial reality.
Shaping intelligence: How a “human in the loop” keeps AI anchored
It’s been said that the only constant in the world is change.
For decades, clinical trials have been a human only endeavor, with teams of clinicians, study teams, and patients working hand in hand to bring the latest molecules to market. Now, a new central actor has entered the clinical paradigm, agentic artificial intelligence.
Only three years after OpenAI kicked off the artificial intelligence arms race, AI has gone from requiring users to prompt it, to pre-emptively identifying bottlenecks, safety risks, and more, thanks to agentic AI.
Agentic AI is an autonomous, goal-oriented system that uses reasoning and external tools to independently plan, execute, and adapt multi-step actions with minimal human intervention to achieve complex objectives.
Sponsors and CROs have begun using AI agents across their workforces to improve trials in ways that humans have traditionally struggled to accomplish. For instance, organizations have been creating AI agents to analyze prior trial protocols, benefiting from lessons learned across prior trials and real-world outcomes, enabling teams to anticipate risks and automating elements of submission drafting. Anomaly detection has helped teams better identify outliers in operational metrics or safety signals, prompting early interventions. Document intelligence accelerates medical writing by grounding generative outputs in verified data, which reduces cycle time without sacrificing accuracy.
However, in each of these use cases, humans remain squarely “in the loop.” Or rather, the decision making isn’t left entirely to AI. Instead, the objective is to augment clinical, regulatory, and legal teams with tools that surface the right information at the right time.
This core concept, keeping a human in the loop, is essential to clinical decision making and operations as agentic AIs, while powerful, are not inherently suited to fully autonomous operation in all regulated contexts.