The field of scientific research is witnessing a significant shift towards the adoption of AI-driven research agents that can accelerate discovery and innovation. These agents are being designed to support researchers in various domains, including pharmaceuticals, materials science, and biomedicine, by providing them with advanced tools for knowledge retrieval, synthesis, and analysis. The development of these agents is driven by the need to overcome the barriers to cross-disciplinary knowledge discovery and synthesis, and to enable researchers to tap into the vast amounts of data and knowledge that are being generated in various fields. The use of AI-driven research agents has the potential to reduce the time and cost associated with scientific discovery, and to enable researchers to explore new areas of research that were previously inaccessible. Noteworthy papers in this area include: The paper introducing DiscoVerse, a multi-agent co-scientist that supports pharmaceutical research and development by implementing semantic retrieval, cross-document linking, and auditable synthesis on a large historical corpus. The paper presenting BioSage, a compound AI architecture that integrates LLMs with RAG and specialized agents to enable discoveries across AI, data science, biomedical, and biosecurity domains. The paper introducing ChatDRex, a conversation-based, multi-agent system that facilitates the execution of complex bioinformatic analyses aiming for network-based drug repurposing prediction.