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Bio-IT World 2025 – The AI Agent Revolution: From AI Interns to AI Experts
Speaking Session: The AI Agent Revolution: From AI Interns to AI Experts
by Jeff Evernham, Chief Product Officer, Sinequa by ChapsVision
Generative AI has started a revolution, and AI Agents are entering the workforce. But to be effective in the enterprise, these agents need accurate data—the data stored in your internal information stores. Sinequa is helping customers deploy intelligent agents in the workplace powered by the best retrieval-augmented generation (RAG) to accelerate innovation, improve results, and assist research by harnessing textual information that has been largely untapped. In this talk, Jeff will explain what agents are, how they’re being used, and what promise the (near) future holds for companies that embrace advanced RAG and move from AI interns to AI experts.
About This Session
At Bio-IT World 2025 — the 24th annual conference convening 3,000+ life sciences, pharma, and healthcare technology leaders in Boston — Sinequa Chief Product Officer Jeff Evernham presented a session on the next frontier of enterprise AI: agents that go beyond answering questions to autonomously executing knowledge work across the vast, largely untapped information stores inside life sciences organizations.
The session title captures the core challenge: most enterprise AI deployments today are stuck at the intern stage — useful, eager, but unable to work independently with the depth and reliability that research scientists, clinical teams, and regulatory professionals actually need. The path from intern to expert runs through one critical capability: accurate, enterprise-grade retrieval.
What This Session Covers
- What AI agents actually are — and aren’t Jeff cuts through the hype to define what an AI agent is in an enterprise context: a system that can reason over information, take multi-step actions, and produce outputs that augment or automate knowledge work — without requiring a human to break down every step. Understanding this distinction matters because most organizations are deploying AI assistants, not AI agents, and the gap in business impact is substantial.
- Why RAG quality determines agent quality AI agents in life sciences are only as reliable as the information they retrieve. To be effective in the enterprise, agents need accurate data — the data stored in your internal information stores. In pharma and biotech, that means ELN systems, clinical trial databases, regulatory submissions, scientific literature, patent filings, and decades of internal research — structured and unstructured, spread across systems that were never designed to work together. Sinequa’s enterprise RAG platform retrieves from all of these simultaneously, grounding every agent output in authorized, current data rather than generic model knowledge.
- The life sciences data problem that makes this hard Unlocking real impact from AI in life sciences requires integrated, multimodal data and infrastructure that supports scientific agility. Life sciences organizations face a specific version of the enterprise data problem: enormous volumes of highly specialized scientific content, strict access controls tied to data sensitivity and regulatory requirements, multilingual data spanning global R&D operations, and a user base — research scientists, clinical statisticians, regulatory affairs teams — whose queries require domain-specific precision that generic search cannot deliver.
- What the near future of agentic AI in life sciences looks like Jeff outlines where AI agents in pharma and biotech are heading: from assistants that answer questions to agents that autonomously monitor literature, flag relevant trial results, draft regulatory sections, and surface expert connections across global R&D teams — with enterprise RAG as the trusted knowledge foundation that makes all of it auditable and safe.
About Bio-IT World
Bio-IT World Conference & Expo is the premier global event showcasing technologies and analytic approaches that solve problems, accelerate science, and drive the future of precision medicine — uniting a community of experts in life sciences, pharmaceuticals, clinical research, healthcare, informatics, and technology. The 2025 conference was a record-breaking event, with more than 2,800 attendees, 310+ speakers, and over 150 exhibitors sharing breakthroughs in the evolution of data-driven drug discovery, precision medicine, genomics, AI, and digital health.
Frequently Asked Questions (FAQ)
An AI assistant responds to queries — it retrieves information and generates answers when prompted by a user. An AI agent goes further: it can reason over information, plan and execute multi-step tasks, and produce outputs autonomously without requiring a human to direct each action. In a life sciences context, an AI assistant might answer a researcher’s question about a compound. An AI agent might autonomously monitor new trial publications, cross-reference them against an internal research database, identify relevance to an active program, and surface a summary to the relevant team — without being asked. The gap in business impact between the two is substantial, and most enterprise AI deployments today are still at the assistant stage.
Retrieval-Augmented Generation (RAG) is the mechanism that grounds an AI agent’s outputs in real enterprise data rather than generic model knowledge. In pharma and life sciences, where decisions are informed by proprietary research, clinical data, regulatory submissions, and specialized scientific literature, an agent that retrieves incorrectly — or fails to retrieve from the right sources — produces outputs that are unreliable at best and dangerous at worst. Enterprise RAG must connect to ELN systems, clinical trial databases, LIMS, regulatory archives, and scientific literature simultaneously, apply access controls so agents only surface data users are authorized to see, and understand scientific language with domain-specific precision. This is what separates an AI intern from an AI expert.
Sinequa’s platform is deployed by life sciences organizations including Pfizer, AstraZeneca, GSK, Novartis, and Bristol Myers Squibb — representing over 50% of the world’s largest pharmaceutical companies. These organizations use Sinequa to connect research scientists, clinical statisticians, regulatory affairs teams, and medical information professionals to the full breadth of their internal and external knowledge assets, powering AI agents and assistants grounded in accurate enterprise data.
Sinequa connects to the full range of life sciences data systems: ELN (Electronic Lab Notebook), LIMS, CDS, regulatory submission systems, clinical trial databases, medical information platforms, SharePoint and document management systems, scientific literature repositories, patent databases, and proprietary research archives — across more than 200 ready-to-use connectors and 350+ data converters. All access controls from source systems are inherited, ensuring that AI agents surface only data each user is authorized to access.
Bio-IT World is the premier annual conference at the intersection of life sciences, pharma, and information technology — convening biopharma, clinical research, healthcare, informatics, and technology leaders to exchange ideas and advance data-driven drug discovery and precision medicine. The 2025 edition drew more than 2,800 attendees and 310+ speakers across 200+ sessions covering AI, generative AI, machine learning, multimodal data, genomics, and digital health. It is the event where life sciences technology strategy is shaped — and where Sinequa presented its vision for moving enterprise AI from basic assistants to expert-level agentic systems.
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