Scientific research agents can do more than retrieve information. They can synthesize studies, work across research sources, execute analysis, and help teams explore complex biomedical questions. The challenge is making those workflows reliable enough to trust. Join our EMEA Deep Life Sci Workshop: Agent Engineering for Life Sciences for a hands-on look at how to build, trace, evaluate, and improve research agents for complex life sciences workflows. 🌍 Register for the EMEA session: https://lnkd.in/gdSeyp25
Oriel Loh
19h
Such an important point. Relying on prompts to enforce scientific rigor usually leads to fragile workflows. By building ScholarGuard on Langchain/Langgraph, we leveraged its orchestration flexibility to design a harness capable of deep-level science. We quickly learned that baking evidence and deterministic trust directly into the architecture is the only way to move from "retrieving information" to workflows scientists can genuinely trust.
This is a fantastic point about reliability in research agents; we've found that ensuring data provenance and fine-grained access controls, even for client-side RAM-only tokenization, is key to maintaining developer velocity without creating regulatory bottlenecks. At Privacy Scrubber, our focus on local de-identification and embedding privacy by design directly addresses these challenges, as detailed in our SOC2 AI privacy controls: https://privacyscrubber.com/compliance/soc2/ai-privacy-controls/ #CISO #ZTDS #AIPrivacy