Agentic AI refers to autonomous AI systems that can independently perform tasks, make decisions, and interact with their environment without constant human supervision. Unlike traditional AI, which often requires human input to generate output, agentic AI can autonomously devise plans, set goals, adapt, and execute strategies on its own.
Agentic AI will further enhance and speed processes related to clinical trials, drug development, and regulatory compliance. It can optimize patient enrollment, monitor real-time data, flag compliance issues, and suggest corrective actions, thereby improving efficiency and accuracy. Going forward, agentic AI will be increasingly capable of predicting probabilty of success as well as sophisciated simulation algorithms to help optimize end-to-end development.
Potential challenges include integrating agentic AI into existing systems, ensuring data privacy and security, mitigating bias, regulatory compliance, and managing the quality and ethical implications of autonomous decision-making. Companies need a clear AI strategy, s systematic approach to enterprise architecture, and human experts in the loop to successfully adopt agentic AI.
AI agents can be designed to act on your behalf, handling time-consuming tasks like literature reviews, data analysis, real-time insights generation, and so much more. By automating tasks, AI agents can free up human teams to focus on high-priority work—enabling them to get more done, faster.
Agentic AI is being used across the entire asset lifecycle, from increasing speed and efficiency of clinical trial start up, to automating literature reviews, to combining a variety of data sources to better prepare field sales for high quality conversations with healthcare providers. Agentic AI is also proving to be beneficial for process automation in general, which can boost efficiency or otherwise streamline processes across life sciences operations.