Agentic AI in Biology: Autonomous AIs Accelerating Scientific Discovery

Chirag Gupta and Ananya Sharma

Nov. 21 2025

Introduction

Imagine an AI that does more than just answer questions, one that can plan experiments, sift through vast datasets, run analyses, and even decide its next steps without being told exactly what to do. This is the promise of agentic AI, a new breed of artificial intelligence where AI systems operate as autonomous agents rather than passive tools. Unlike traditional AI that only provides insights when prompted, an agentic AI can set goals, make plans, execute tasks, and adapt on the fly without constant human supervision. In 2023, experimental projects like AutoGPT gave a glimpse of this potential, showing how a language model could chain together tasks and act with a degree of independence. Now, beyond writing emails or booking travel plans, these agentic AI systems are being harnessed to tackle some of the hardest problems in science and biology.

This article provides a high-level exploration of what agentic AI is and how it's transforming biological research and data analysis. We'll look at how these AI "agents" work, the kinds of breakthrough applications across biology, from drug discovery to personalized medicine, and discuss the benefits, challenges, and future of this technology. The aim is not to dive into programming frameworks or technical jargon, but to give a clear overview for the general reader of how AI agents are becoming partners in scientific discovery.

What is Agentic AI (and How Does it Work)?

In simple terms, agentic AI refers to AI systems endowed with a level of agency as they can independently decide what actions to take to achieve given objectives. Instead of waiting for a specific prompt for each step, an agentic AI is goal-directed, able to break complex tasks into subtasks, invoke tools or databases as needed, and adjust its strategy based on results. In essence, it behaves like a proactive problem-solver. For example, whereas a traditional chatbot might only answer questions it's asked, an agentic AI could be told "find a cure for X" and then autonomously formulate hypotheses, run virtual experiments, gather information, and iterate. All while checking in with the human when necessary.

Modern agentic AIs are often built on powerful large language models (LLMs) (like the technology behind ChatGPT) but augmented with additional capabilities. These AI agents can retrieve information, use external tools, run code, and even communicate with other AI agents or humans in natural language. This means an agent can read scientific literature, control lab robots, analyze spreadsheets, or call APIs in order to accomplish its goals. Under the hood, techniques like "chain-of-thought" prompting, memory modules, and tool integration enable the AI to carry out multi-step reasoning processes. Crucially, the AI maintains context and can decide when it has enough information or when to ask for guidance, rather than needing a human to micromanage every step.

It's important to note that agentic AI is designed to complement human scientists, not replace them. In fact, researchers envision "AI scientists" that work alongside humans: these agents combine human creativity and expertise with AI's ability to analyze giant datasets, explore countless hypotheses, and execute repetitive tasks at scale. The AI might handle the heavy lifting of data-crunching and trial-and-error, freeing up humans to focus on big-picture ideas, interpretation, and creative design. As one team put it, rather than taking humans out of the loop, the goal is to augment human researchers with tireless, knowledgeable AI collaborators.

A Digital Lab Partner

To illustrate, think of an agentic AI as a digital lab partner or co-scientist. Such an AI agent can identify knowledge gaps, propose experiments to fill them, interface with laboratory instruments or software, analyze data from the experiments, learn from the outcomes, and then refine its approach iteratively. This goes far beyond a conventional software tool. For example, an AI agent could notice that no one has tested a certain protein for a drug target, suggest an experiment to test it, control a lab robot to run the experiment, interpret the results, and then decide the next experiment, all with minimal human input.

In practice, researchers are beginning to build such sophisticated agents. A recent effort at Stanford introduced "Biomni," a general-purpose biomedical AI agent designed to autonomously execute a wide spectrum of research tasks across many biology and medicine subfields. Biomni integrates a large language model with over a hundred specialized tools and databases, allowing it to dynamically plan and carry out complex biomedical workflows without relying on rigid scripts. In tests, Biomni demonstrated the ability to generalize across tasks ranging from prioritizing disease-related genes and repurposing existing drugs, to diagnosing rare diseases and even planning molecular cloning experiments without task-specific coaching. This flexibility hints at what's now possible: AI agents that can traverse the breadth of biomedical knowledge, tying together genetics, chemistry, clinical data, and more on their own.

Notably, agentic AIs can also work together. Researchers often deploy multiple agents in a collaborative framework, each specializing in a role. For instance, one agent might excel at literature review, another at experimental design, another at data analysis, all coordinated by a "leader" agent. A recent "virtual lab" project from Stanford exemplified this by assembling a team of AI agents modeled after a real science team: an AI Principal Investigator oversaw the project and spawned specialist agents (for immunology, computational biology, machine learning, etc.), plus a "critic" agent whose job was to question ideas and flag potential errors. This AI lab was given a challenge to devise a better vaccine for a virus. Within days the AI agents had generated novel vaccine design ideas that went beyond what human experts had published, impressing the researchers. Such multi-agent setups underscore that agentic systems aren't one monolithic AI doing everything, but often a network of AIs cooperating (and even constructively criticizing each other) to solve complex problems.

Applications of Agentic AI in Biology and Medicine

How are these autonomous AI agents actually being used in biology and related fields? Below we explore some real and potential examples across different domains of the life sciences, from basic research to healthcare. These illustrate the breadth of agentic AI's impact, showing how AI agents can take on many roles that traditionally required extensive human effort.

Agentic AI in Scientific Research: Five Use Cases

Automated Literature Explorer: An AI agent can serve as an ever-vigilant research assistant, continuously scanning new scientific publications, papers, and databases to extract relevant information and summarize findings for researchers. Instead of manual literature searches, an agent can keep a biology lab up to date on the latest discoveries. For example, an autonomous literature agent could monitor all new papers on a specific cancer pathway and immediately alert a drug discovery team to breakthroughs in kinase inhibitors, complete with summaries of the results. This dramatically speeds up knowledge gathering.
Hypothesis Generator: By analyzing large datasets and identifying hidden patterns, agentic AIs can propose new scientific hypotheses or research directions that scientists might not have considered. The agent might integrate genomics data, clinical data, and prior studies to spot non-obvious correlations. For instance, an AI looking at cancer genomics could suggest novel gene targets for therapy, pointing researchers to genes that, say, haven't been studied in breast cancer but show unusual patterns in big genomic databases. Such AI-generated hypotheses give scientists a richer starting point for investigations.
Experimental Design Optimizer: Designing a good experiment (choosing what variables to test, which controls to use, etc.) is often an iterative, time-consuming art. An autonomous agent can craft and refine experimental protocols by learning from prior results and using machine learning to optimize each step. The agent might select the most promising conditions or candidates to test next, dynamically improving the experimental plan. For example, in a gene-editing project, an AI agent could prioritize which CRISPR gene edits are most likely to yield a desired trait in the next experiment cycle. By intelligently narrowing the options, it accelerates the discovery of effective solutions.
Simulation & In Silico Testing Agent: Before scientists invest time and money in physical experiments, AI agents can run virtual experiments and simulations. These agents use computational models of biological systems to predict outcomes, essentially acting as a filter to find promising leads. One practical use is virtual drug screening: an AI agent can simulate how thousands of potential drug molecules might interact with a target protein, quickly shortlisting the top candidates for real-world testing. Similarly, agents might simulate metabolic networks, ecological systems, or evolutionary scenarios to suggest what is worth trying in vivo or in vitro.
Lab Automation Coordinator: Perhaps the most transformative is an agent that bridges AI with robotics – orchestrating lab equipment and automated instruments to carry out experiments autonomously. In a "smart lab," such an AI agent could plan an experiment, then directly control robots, pipettes, sequencers, or microscopes to execute the procedure, all while logging data. For example, an agent could manage a complete protein purification workflow: from instructing a robotic arm to prepare samples, to running the purification protocol, to collecting and analyzing the output data in a lab notebook. The entire experiment could be done start-to-finish by the AI, with the human scientist simply overseeing the process or focusing on interpreting results.

These use cases are not science fiction – they are actively being developed. In fact, researchers have already demonstrated a prototype of a "self-driving lab" for biology. In one case, a team at the University of Illinois built an autonomous system to improve enzymes used in industry: the AI agent would propose beneficial genetic mutations to an enzyme, the automated robots in the lab would synthesize and test those mutant enzymes, the results would feed back into the AI, and then the cycle would repeat without manual intervention. As one scientist described it, "It's a step toward a self-driving lab: a lab that designs its own proteins, makes the proteins, tests them and makes the next one", where "the designing and learning is done by an AI algorithm, and the building and testing is done by robotics". In their demonstration, the agent-guided system produced enzyme variants with dramatically improved performance (one enzyme's activity was boosted 26-fold) in a fraction of the time it would have taken by trial-and-error. This hints at how agentic AI can accelerate bioengineering and biotechnology tasks that traditionally take months or years.

Beyond the Lab: Clinical and Healthcare Applications

Agentic AI is also extending into biomedical and health settings beyond the research bench. For example, in clinical trials, autonomous AI agents can analyze patient databases and electronic health records to match eligible patients with ongoing clinical trials, automate parts of the patient recruitment process, and even assist in drafting trial protocols and paperwork. This can make trials faster and more efficient. In fact, early implementations show that AI agents can comb through complex criteria and find matching patients far more quickly, improving enrollment rates and diversity in trials.

In healthcare and personalized medicine, we're starting to see AI agents act as smart assistants for doctors and patients. Imagine a virtual health agent that continuously monitors a diabetic patient's glucose readings, diet, and exercise data (along with relevant medical literature), and autonomously adjusts the patient's insulin dosing plan or provides tailored advice in real time and alerts a human doctor if something needs an expert decision. Agents like this are on the horizon: able to integrate genetic information, clinical history, and real-time sensor data to tailor treatment plans for individuals. In hospitals, agentic AI systems might manage routine tasks like drafting discharge summaries, scheduling follow-ups, or flagging anomalies in patient vitals that need attention. The common thread is that these agents can coordinate complex, multi-step workflows in healthcare, functioning like diligent digital interns for the medical team. (Notably, conversational madicaly savvy health agents are an early form of this, assisting patients with questions and providing health monitoring support.)

It's worth noting that the agentic approach isn't limited to biology and medicine. Similar autonomous agents are emerging in chemistry and materials science, where, for instance, specialized AI agents can design and test new molecules or materials with desired properties. The life sciences, however, stand to benefit enormously because of the extreme complexity of biological systems. Agentic AIs are well-suited to handle complexity by tirelessly integrating data and iterating experiments.

Benefits of Agentic AI in Scientific Discovery

Why are scientists and biotech companies excited about agentic AI? Implemented well, these autonomous AI collaborators offer several key benefits:

Efficiency and Productivity: Automated agents can dramatically speed up research cycles that normally take humans weeks or months. They work 24/7 and can execute many tasks in parallel. Early studies and implementations report significant time and cost reductions, in some cases cutting experimental costs by ~30% thanks to optimized planning and automation. By handling the grunt work, agents let human researchers accomplish more in less time.
Deeper Insights from Big Data: Biology today is awash in big data (genomic sequences, proteomic data, medical records, imaging, etc.). AI agents excel at finding non-obvious patterns in huge, complex datasets that would overwhelm human analysis. They can integrate diverse data types (text, numbers, images) to uncover connections. This often leads to richer scientific insights, as the agent might highlight a subtle gene-disease link or multi-factor trend that humans missed.
Improved Reproducibility: When an AI agent runs an experiment or analysis, it will do so consistently each time, following the protocol exactly. This helps standardize procedures. Automated execution means fewer random errors (no pipetting mistakes, no forgotten steps), which in turn improves the reproducibility of scientific results. Every step an agent takes can be logged in detail, creating a clear audit trail.
Accessibility of Advanced Techniques: Traditionally, cutting-edge experimental design or analysis might require experts at top institutions. Agentic AI has the potential to make high-level research strategies more accessible to smaller labs or companies. For example, a small biotech startup could leverage an AI agent to design experiments as well as a whole team of veteran scientists, effectively "democratizing" some expertise. This could level the playing field in innovation.
Human–AI Collaboration and Creativity: When routine tasks are offloaded to AIs, human scientists can focus on creative thinking, theory, and interpretation. This shifts researchers toward higher-value work, with the AI handling execution. Furthermore, working with an AI that generates ideas or alternatives can spark new lines of thought for humans. The agent becomes a creative partner, brainstorming along with the team. The net effect is often a more collaborative and interdisciplinary research process, as AI agents can also act as bridges between fields (e.g., linking biology and computer science knowledge).

Challenges and Risks

As promising as agentic AIs are, adopting them in biological research and medicine comes with significant challenges and risks that must be addressed:

Trust and Verification: Scientists need to trust the AI's outputs, which means the agent's reasoning should be transparent and results must be rigorously validated. If an agent suggests a new drug target or designs an experiment, how do we know its suggestion is reliable? Black-box AI recommendations can be flawed, so interpretable and verifiable AI output is essential. Researchers are working on ways to make AI agents explain their reasoning and to establish evaluation benchmarks for their performance.
Bias and Data Quality: AI systems can inadvertently amplify biases present in their training data. In a biomedical context, this could mean an agent that's read mostly Western genomic studies might overlook variants important for other populations, for example. There's a risk of biased hypotheses or errors if the agent's knowledge base isn't representative or if the data it learns from is flawed. Careful curation of training data and ongoing human oversight are needed to mitigate this.
Privacy and Security: In healthcare applications, agents may handle sensitive patient data. Stringent data privacy safeguards and compliance with regulations (like HIPAA) are non-negotiable. Moreover, a malicious agent or an agent given faulty instructions could do harm, for instance, by recommending a toxic experiment. Ensuring robust security and fail-safes (so that agents don't overstep bounds or are not misused) is a key challenge.
Regulatory and Ethical Hurdles: The use of autonomous AI in regulated fields (medicine, pharma) raises questions for regulators. How do we certify an AI-designed experiment or an AI-chosen clinical trial cohort? Existing frameworks may need updates to account for AI-driven decisions. Ethically, we must consider accountability. If an AI agent causes a bad outcome, who is responsible? Until oversight catches up, fully autonomous labs (sometimes dubbed "hands-off" labs) might be risky, especially in high-stakes areas like medical diagnostics.
Human Oversight and the Role of Scientists: There is a delicate balance to strike in how much autonomy to give AI agents. Totally hands-off science is not yet advisable, as human expertise is still crucial to catch AI mistakes or ethical issues. Researchers will need to adopt new roles, supervising and guiding AI agents (almost like managing a very powerful but somewhat unpredictable junior colleague). Training scientists to effectively work with and oversee AI agents will be important. Organizations will also need to develop guidelines for this new mode of work to ensure scientific integrity is maintained even as agents take on more tasks.

In short, rigorous evaluation, oversight, and governance must accompany the technical development of agentic AI. The scientific community is actively discussing these challenges and developing standards (for example, for benchmarking AI agent performance) to ensure we can reap the benefits of autonomy without compromising on quality or ethics.

Future Outlook

Agentic AI in biology is advancing rapidly, and the coming decade is likely to bring even more remarkable developments. Here are a few ways this technology might evolve and shape the future of science:

Hybrid Teams of Humans and AI: We will see research teams that are part human, part AI. Routine work and even some creative design will be handled by AI agents, while human scientists provide strategic direction and deep insight. This collaboration could become a standard mode of research, much as computational tools are standard today.
Explainable and Self-Auditing AI: Future AI agents will likely have built-in explainability, able to articulate why they made certain recommendations (e.g. "I suggested this drug target because of X, Y, Z evidence"). Agents may also become self-reflective. Assessing their own confidence in a result and flagging when something seems uncertain or possibly flawed. This could greatly increase human trust in their autonomy.
Seamless Lab Integration: Agentic AI will be tightly integrated with laboratory infrastructure. We can expect AI-native lab notebooks and information management systems (LIMS) that allow agents to directly log data, retrieve experiment history, or adjust protocols in real time. Fully automated "lab of the future" setups, where multiple agents coordinate everything from hypothesis to experimentation to analysis, are on the horizon.
Multi-Agent Collaborations: Just as large research labs have many specialists, future AI might consist of multiple specialized agents working in concert, mirroring a research ecosystem. One agent might be an expert in microscopy image analysis, another in genomic sequencing, another in statistical modeling, all communicating among themselves to jointly tackle a project.
Autonomous Discovery Engines: As these systems improve, we could see AI-driven discovery platforms that operate at speeds and scales far beyond human capability. Imagine an AI system generating thousands of hypotheses overnight, testing them in silico or even in automated wet-labs by morning, and outputting a shortlist of promising new discoveries by breakfast. This could push scientific progress to an unprecedented pace, potentially leading to breakthroughs (new drugs, biofuels, crop improvements, etc.) much faster than the traditional timeline.

All told, the trajectory of agentic AI suggests it will become an everyday part of how science is done, essentially empowering researchers. Major research conferences and journals are already spotlighting breakthroughs in this area, and investment is pouring into "AI for science" initiatives. With each success, comfort with autonomous systems grows, and so do expectations of what they can achieve.

Conclusion

Not long ago, the idea of an AI autonomously designing and executing experiments sounded like science fiction. Today, agentic AI is actively transforming how scientific inquiries are conceived, conducted, and completed. From generating hypotheses and planning experiments to carrying out lab work and analyzing data, AI agents are becoming true collaborators in the scientific process, effectively a new kind of researcher at the table. This shift could well be one of the defining innovations of our era, opening a new frontier in biological discovery and innovation.

The challenge now is not whether scientists should embrace agentic AI, but how to integrate these agents responsibly and effectively. Researchers and organizations must refine best practices to ensure proper oversight, accountability, and ethical use of autonomous AI. When an AI agent proposes an experiment or a treatment, humans must be ready to question, interpret, and guide it. If done thoughtfully, however, the payoff is enormous. Agentic AI stands poised to become a cornerstone of discovery in biology. Agents will turbocharge research, enable breakthroughs that were previously out of reach, and reshape what's possible in our quest to understand and improve life.

Ultimately, the story of agentic AI in biology is one of partnership: humans and intelligent machines working side by side to push the boundaries of knowledge. By pairing our curiosity and creativity with the tireless rigor of AI agents, we are entering an era where scientific progress may be limited less by our tools and more only by our imagination.

Anatomy of an AI agent (rendered using nanobanana)