DistillerSR Agentic AI
Recorded Webinar
DistillerSR Agentic AI FAQ
What is agentic AI and how is it different from traditional AI?
Agentic AI is a system that acts autonomously to achieve goals by using the right tools at the right time. Unlike traditional AI that simply responds to direct prompts, agentic AI systems are goal-oriented and can make decisions to accomplish complex tasks. The technology is built on large language models (LLMs) but goes a step further by leveraging reasoning chains to use external tools, such as databases and APIs, to solve real-world problems.
What problems in life sciences does agentic AI with DistillerSR aim to solve?
The first problem it addresses is evidence silos in life sciences organizations. Different teams—like those in R&D, regulatory affairs, and medical affairs—often collect and analyze evidence in isolation. This leads to costly rework, inconsistent evidence quality, rising research costs, and delayed decision-making. By centralizing high-quality, validated literature and making it accessible across the organization via DistillerSR Agentic AI, you break down these silos, democratize access to data, and accelerate the time it takes to get critical insights.
Second, agentic AI significantly improves efficiency and reduces costs in literature based research. It can help with every step of the process, from developing search terms and checking inclusion criteria to evidence synthesis and drafting sections of clinical evaluation reports. The technology can also instantly generate summaries, compare different projects, and retrieve supportive data, which can take days or weeks to do manually. A McKinsey projection mentioned in the transcript suggests that agentic AI could free up 25-40% of employee workloads in pharmaceutical and medical device companies.
How does DistillerSR ensure the reliability and trustworthiness of its agentic AI?
DistillerSR grounds its agentic AI responses in expertly validated literature evidence. This approach addresses the issue of “hallucinations” and inaccuracies often seen with off-the-shelf LLMs by ensuring that all outputs, summaries, and reports are based on a curated repository of validated literature evidence. The company also adheres to strict compliance programs like SOC 2 Type II and GDPR, and follows the NIST AI Risk Management Framework to ensure ethical, secure, and responsible AI development.
Can agentic AI be integrated with other internal tools?
What are some best practices for using agentic AI with DistillerSR?
To get the best results, you should:
- Be clear and direct: Use specific details in your queries, such as project names, to avoid AI confusion.
- Provide context: Update your system with all the information you want it to consider.
- Structure your prompts: Break down complex tasks into smaller, more manageable steps by using multiple prompts.
- Iterate and refine: Review the output from each step and make adjustments as needed.
- Be mindful of limitations: Understand the specific limitations of the tool, such as not being able to bulk delete references, to ensure a more trusted and effective experience.