Agentic Research: The Future Scientist’s Workspace

The best way to imagine the future of science is to step into it. It is 2026, and Dr. Franklin, a translational scientist researching inflammatory pathways, begins her morning with a question that would have once taken months to explore. Here is how that day unfolds.

A Day in the Life of Dr. Franklin, 2026

Morning recall. Dr. Franklin begins her day by logging into Agentic Research. Overnight, her workspace has revived yesterday’s context and gathered 312 relevant papers, 17 GEO datasets, and multiple proteomics studies from various sources - both internal and external. Instead of spending time searching for information, she begins her work with a complete view of all the available data.

Hypothesis canvas. She asks: “Could partial NLRP3 blockade restore podocyte autophagy without compromising antiviral immunity?” The Planner agent classifies the question and summons three specialists: a Pathway Enrichment agent, a Risk Simulation agent (utilizing innate-immunity heuristics), and a Cohort Visualisation module to ground the hypothesis in human data.

The module gathers relevant datasets (GEO, single-cell kidney atlases, internal registries), harmonizes them, and maps the DEG list into patient/sample data. It then computes pathway and interferon signature scores, stratifies subgroups, and highlights potential confounders where metadata exist. Within minutes, it generates an interactive notebook with plots, references, and editable code.

Predict-and-design loop. Next, she requests structural predictions and fragment hotspots. Agentic Research runs the models, evaluates binding potential, and flags prior research or patents. While she grabs coffee, a biology reasoning model analyzes her dataset, overlaying known autophagy regulators and surfacing unexpected connections.

Collaboration snapshot. Later in the day, a colleague in Boston adds proprietary scaffolds into her notebook. The Notebook orchestrator tracks the edit with version history and differences. Meanwhile, compliance agents automatically check software versions and data licenses, flagging any gaps.

End-of-day wrap. At 17:00, her team gets a summary from Agentic Research with key findings, potential risks, and links to all the related discussion threads. Anyone on the team can replay the analysis at double speed or clone the notebook to start a new study.  

The change Dr. Franklin experienced is not just gradual; it’s a significant shift in how desk research will happen. Work that once required weeks of hand-offs between teams, disparate data sources, and a tangle of tools now happens in a single, live workspace. In this workspace, prior-knowledge retrieval, hypothesis generation, multi-omics validation, risk assessment, and even creating slide-ready materials all run in one continuous agentic workflow, with a human overseeing the process.

Agentic Research introduces a new way of working, enabling scientists and teams to handle more of the scientific processes in one place. It's where silos are broken down, hypotheses are generated and confirmed faster, and collaboration accelerates.

This future is not just possible, it is inevitable

Generative AI gives us new ways to think and interact. It goes beyond just searching and answering questions. The graph below shows this change. It illustrates how adoption has grown from simple query tasks to a broad range of applications.

The most compelling story is not GenAI alone, but how it has acted as a catalyst for breakthroughs across multiple domains, including foundational models in language, biology, and chemistry, as well as predictive AI. With new capabilities and interfaces for computational modelling and simulations, and the rise of agentic AI, which is creating a shift from productivity and task automation to goal-driven collaboration between humans and Artificial Intelligence.  

When multiple related fields accelerate and advance substantially, the question to ask is - how will this technology converge? How does the future look like? Will it be shaped by capabilities fusing into something entirely new?

A useful analogy is the run-up to the modern smartphone. The breakthrough wasn’t any single event; It was an advancement in telecommunications, chips, capacitive touchscreens, GPS, batteries, and many others, converging into a single system - hardware and mobile operating system orchestrating this complexity into a device that re-defined our social and professional lives.  

For science, this state of convergence is Agentic Research. Labs, pharma R&D, AI vendors, and open-source communities will continue to innovate. Existing capabilities will improve, new ones will emerge, interfaces will align, and we all will relentlessly try to stitch them together into something more powerful, until the seams disappear. Agentic convergence is not hypothetical; it is the scientist's new workspace.  

What is Agentic Research

The scope of Agentic Research is multidimensional - it encompasses both the orchestration layer and the scientist’s workspace, a coordination of reasoning LLMs, domain foundational models, and expert agents over a unified scientific data and tool fabric.  

What this enables is not another chat layer or Retrieval Augmented Generation interface, but an end-to-end working surface: hypothesise → plan / design → retrieve → reason → simulate → validate → communicate. The entirety of scientific inquiry will be transformed by Agentic Research:

• Knowledge: Finding prior evidence becomes a context-aware search across literature, multi-omics, patents, and proprietary assays, powered by ontology-aware tools.

• Hypothesis: Idea generation shifts from free-text brainstorming to constraint-guided dialogue. Hypotheses only surface if they pass pathway plausibility checks and cohort-level statistics embedded in knowledge graphs and biological networks.

• Validation: New datasets, docking scores, simulation results, protein structure predictions, and toxicity checks are automatically integrated into the canvas, strengthening or weakening claims and making prioritization faster and more rigorous.

• Discovery & Design: Serial hand-offs between bioinformaticians, modellers, and chemists evolve into co-creation on the same canvas. Agents run simulations, users tweak parameters, and revise research plans.

• Communication: Reports and slides move from manual copy-paste to auto-compiled artifacts. Methods, figures, and decks carry embedded citations and executable code, allowing collaborators, reviewers, and regulators to reproduce analyses instantly.

Implications for R&D

Agentic Research will reshape the economics of discovery. The marginal cost of testing ideas falls dramatically, opening space for more creativity, exploration, and shots on target. The ability to generate higher-quality hypotheses faster, compounded with an increase in the velocity of validating them, will drive key outcomes, including the success rate of discovery programs and time to market.

In a recent PwC survey, 79% of senior executives reported adopting agentic AI within their organizations, and 88% plan to increase their AI budgets in the coming year. Leaders recognize the potential of the technology and, at some level, anticipate the agentic convergence.

Some organizations are already reaping the benefits. One leading biopharma company reports 25% faster cycle times in lead generation and 35% efficiency gains in drafting clinical study reports after integrating agentic AI into their workflow. We expect to see a growing number of reported improvements in research outcomes as more capabilities come together into orchestrated systems, which get adopted.

Journey to Agentic Research

The Day in the Life of Dr. Franklin scenario is an ambitious vision of the future of scientific work, a journey that is not a straight line. There are a few practical challenges and considerations worth making today.

• Engineering orchestration is key to building a system that can take an input and produce an output as a function of a complex agentic interplay with a human in the loop, bound by enterprise requirements, regulatory constraints, and scientific rigour standards. Thoughtful systems need to be architected to build resilience into volatile multi-agent systems, based on auto-regressive LLMs. These systems must adhere to human R&D principles, manage agentic interfaces, error handling, and provenance.

• Agentic decision-making framework will determine whether these systems are useful and impactful. Agents will make numerous decisions - how to create new hypotheses, how to break down complex questions, which tools to use from a library of hundreds, and how to optimise queries. They will need persistent decision-making gates with critique, optimization, and evaluation loops involving humans and other agents. Deep expertise in scientific use cases and workflows, research principles, and “what good looks like” for every work output - will be key to building valuable products.

• Adoption and collaboration will be crucial to realise the full potential of agentic systems. Individual scientists and organisations will be challenged by a new way of working, but adopting this approach will help them adjust, extract value, and transform the research landscape.

Describing a vision of the future in which you believe is the first important step. Betting on this future will make you a part of shaping it and building what will become the operating system of modern science.

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