# Empowering Biomedical Discovery with AI Agents

Shanghua Gao  Department of Biomedical Informatics, Harvard Medical School,
Boston, MA, USA  Ada Fang  Department of Biomedical Informatics, Harvard
Medical School, Boston, MA, USA  Department of Chemistry and Chemical Biology,
Harvard University, Cambridge, MA, USA  Kempner Institute for the Study of
Natural and Artificial Intelligence, Harvard University, MA, USA  Co-second
authors Yepeng Huang  Department of Biomedical Informatics, Harvard Medical
School, Boston, MA, USA  Program in Biological and Biomedical Sciences,
Harvard Medical School, Boston, MA, USA  Co-second authors Valentina
Giunchiglia  Department of Biomedical Informatics, Harvard Medical School,
Boston, MA, USA  Department of Brain Sciences, Imperial College London,
London, UK  Co-second authors Ayush Noori  Department of Biomedical
Informatics, Harvard Medical School, Boston, MA, USA  Harvard College,
Cambridge, MA, USA  Co-second authors Jonathan Richard Schwarz  Department of
Biomedical Informatics, Harvard Medical School, Boston, MA, USA  Yasha
Ektefaie  Department of Biomedical Informatics, Harvard Medical School,
Boston, MA, USA  Program in Biomedical Informatics, Harvard Medical School,
Boston, MA, USA  Jovana Kondic  Department of Electrical Engineering and
Computer Science, MIT, Cambridge, MA, USA  Marinka Zitnik  Department of
Biomedical Informatics, Harvard Medical School, Boston, MA, USA  Kempner
Institute for the Study of Natural and Artificial Intelligence, Harvard
University, MA, USA  Broad Institute of MIT and Harvard, Cambridge, MA, USA
Harvard Data Science Initiative, Cambridge, MA, USA

###### Summary

We envision “AI scientists” as systems capable of skeptical learning and
reasoning that empower biomedical research through collaborative agents that
integrate machine learning tools with experimental platforms. Rather than
taking humans out of the discovery process, biomedical AI agents combine human
creativity and expertise with AI’s ability to analyze large datasets, navigate
hypothesis spaces, and execute repetitive tasks. AI agents are proficient in a
variety of tasks, including self-assessment and planning of discovery
workflows. These agents use large language models and generative models to
feature structured memory for continual learning and use machine learning
tools to incorporate scientific knowledge, biological principles, and
theories. AI agents can impact areas ranging from hybrid cell simulation,
programmable control of phenotypes, and the design of cellular circuits to the
development of new therapies.

##  1 Introduction

A long-standing ambition for artificial intelligence (AI) in biomedicine is
the development of AI systems that could eventually make major scientific
discoveries, with the potential to be worthy of a Nobel Prize—fulfilling the
Nobel Turing Challenge [1]. While the concept of an “AI scientist” is
aspirational, advances in agent-based AI pave the way to the development of AI
agents as conversable systems capable of skeptical learning and reasoning that
coordinate large language models (LLMs), machine learning (ML) tools,
experimental platforms, or even combinations of them [2, 3, 4, 5] (Figure 1).

The complexity of biological problems requires a multistage approach, where
decomposing complex questions into simpler tasks is necessary. AI agents can
break down a problem into manageable subtasks, which can then be addressed by
agents with specialized functions for targeted problem-solving and integration
of scientific knowledge, paving the way toward a future in which a major
biomedical discovery is made solely by AI [2, 6]. In the near future, AI
agents can accelerate discovery workflows by making them faster and more
resource-efficient. Looking ahead, AI agents can enable insights that might
not have been possible using ML alone by making predictions across temporal
and spatial scales prior to experimental measurements at those scales, and can
eventually identify new modes of behavior within biological systems [6].

This vision is possible thanks to advances in LLMs [7, 8, 9], multimodal
learning, and generative models. Chat-optimized LLMs, such as GPT-4 [10], can
incorporate feedback, enabling AI agents to cooperate through conversations
with each other and with humans [11]. These conversations can involve agents
providing and seeking advice, critique, and validation [12, 13]. Then, since a
single LLM can exhibit a broad range of capabilities—especially when
configured with appropriate prompts and inference settings, conversations
between differently configured agents can combine these capabilities in a
modular manner [14]. LLMs have also demonstrated the ability to solve complex
tasks by breaking them into subtasks [15, 16]. However, suppose we follow
conventional approaches to foundation models such as LLMs and other large pre-
trained models. In that case, we may not develop AI agents that can generate
novel hypotheses because such novelty would not have been in the data used to
train the model, suggesting that current foundation models alone are not
sufficient for “AI scientists”. Using LLMs as a comparison, generating novel
hypotheses requires creativity and grounding in scientific knowledge, whereas
generating novel text requires adherence to semantic and syntactic rules [17],
so the latter approach aligns well with techniques for next-token prediction
within LLMs, while the former does not.

Here, we offer a perspective that “AI scientists” can be realized as AI agents
backed by humans, LLMs, ML models, and other tools like experimental platforms
that cooperate to solve complex tasks. An AI agent should be skeptical when
developing biological hypotheses, capable of characterizing its uncertainty
and using that as a driver to acquire and refine its scientific knowledge
bases in a way that human scientist partners can trust [18]. AI agents should
be designed to adapt to new biological insights, incorporate the latest
scientific findings, and refine hypotheses based on experimental results. This
adaptability ensures agents remain relevant in the face of rapidly evolving
biological data and understanding [17]. However, the challenge lies in
developing algorithms that allow for this continual learning without suffering
from catastrophic forgetting [19], which requires a balance between acquiring
new knowledge and retaining existing information. By addressing this
challenge, AI agents can power platforms, such as closed-loop synthesis
frameworks, autonomous microscopy facilities, and hybrid cell simulators.

Ethical considerations arise from using AI agents [20, 21]. Allowing AI agents
to make changes in environments through tools or calls to experimental
platforms can be dangerous. Safeguards and protocols need to be in place to
prevent harm and negative outcomes [22]. Conversely, discovery workflows might
include conversations between AI agents (but no interaction with environments
is allowed). In that case, we need to consider the impact of such interactions
on scientists and their reliance on AI agents. The impact of AI agents on
biology, coupled with the challenges they pose, underscores the need for
responsible implementation. AI agents have the potential to perform research
and operational tasks under the oversight of humans [2].

##  2 Evolving use of data-driven models in biomedical research

Data-driven models have reshaped biomedical research over the past several
decades through the development of databases, search engines, machine
learning, and interactive and foundation learning models (Figure 2). These
models have advanced modeling of proteins [23, 24, 25, 26, 27], genes [28],
phenotypes [29], clinical outcomes [30, 31, 32], and chemical compounds [33,
34] through mining of biomedical data.

Databases and search engines. In biological research, databases (DBs) [35, 36,
37] aggregate knowledge from experiments and studies, offering searchable
repositories containing standardized biological data vocabularies. An example
of such a database is the AlphaFold Protein Structure DB [38], which includes
more than 200 million protein structures predicted by AlphaFold [39].
Molecular search engines retrieve information from these databases [40, 41,
42]. FoldSeek [43] retrieves protein structures from the AlphaFold DB by
translating query structures into 3D interaction alphabet sequences and using
pretrained substitution matrices.

Distinct from search engines, designed for retrieving information based on
specific queries, AI agents are capable of reasoning to formulate search
queries and subsequently acquire information. Curated databases offer
structured and factual information, aiding in reducing the risks associated
with misinformation potentially generated by agent hallucinations [44]. A
notable feature of these agents is their ability to dynamically retrieve
information when needed and to create and reflect upon the obtained passages.
This reflection process renders the agent controllable during inference,
allowing for customization of its actions to meet the requirements of tasks
beyond what is possible using search engines and database queries.

Machine learning models. Beyond information retrieval, ML models excel in
identifying patterns and assimilating latent knowledge to generalize
predictions about novel data [45, 46, 47]. An example is AlphaFold [39]
protein folding model that uses multi-sequence alignment with a deep learning
model to predict the 3D protein structure from amino acid sequences, achieving
near-atom level accuracy. AI agents represent an evolution in ML models,
building on the foundations of successes such as the transformer architecture
[48] and generative pretraining [9]. These agents have reasoning and
interactive capabilities that distinguish them from ML models, which typically
require specialized models for each task. Unlike traditional ML models, agents
assess the evolving environment, which is valuable for modeling dynamic
biological systems.

Interactive learning models. Interactive learning, often referred to as active
learning [49] and reinforcement learning [50], represents a further
advancement in ML models. This method enhances the adaptability and efficiency
of ML techniques by incorporating exploration and feedback mechanisms. Active
learning focuses on efficiently training models with limited labeled data,
proving particularly advantageous in biological learning where data may be
insufficient. It selectively queries the most informative data points for
labeling and optimizing the learning process, which improves how models learn
with data. Reinforcement learning involves an agent learning how to act by
observing the results of past actions in an environment, mirroring the trial-
and-error approach. In biological research, interactive learning has been
applied to various fields, such as small molecule design [51], protein design
[52, 53], drug discovery [54, 55], perturbation experiment design [56], and
cancer screening [57]. For instance, GENTRL [51] uses reinforcement learning
to navigate the chemical space and find candidate chemical compounds that can
act against a biological target.

Leveraging interactive learning, AI agents achieve greater autonomy in
information retrieval tasks. Active learning improves training efficiency
through data labeling selected to maximize model performance. However, AI
agents extend beyond this data-centric approach; for example, reinforcement
learning with human feedback (RLHF) [50] uses a “reward model” to train and
fine-tune an LLM-based agent with direct human feedback to understand human
instruction naturally.

AI agents. Looking ahead, AI agents have advanced capabilities, including
proactive information acquisition through perception modules, interaction with
tools, reasoning, and the capability to engage with and learn from their
environments. Agents use external tools, such as lab equipment, and have
perception modules, such as integrated visual ML tools to receive information
from the environment (Section 5). Agents can incorporate search engines and ML
tools and process information across data modalities via perception modules to
generate hypotheses and refine them based on scientific evidence [2, 3].

##  3 Types of biomedical AI agents

The prevailing approach to building agents nowadays is to use LLMs, where a
single LLM is programmed to perform various roles (Section 3.1). However,
beyond LLM agents, we envision multi-agent systems for discovery workflows
that combine heterogeneous agents (Figure 1) consisting of ML tools, domain-
specific specialized tools, and human experts (Section 3.2). Given that much
of biomedical research is not text-based, such agents have broader
applicability to biomedicine.

###  3.1 Large language model based AI agents

Programming a single LLM with diverse roles equips LLM-based agents with
conversational interfaces that emulate human expertise and can access tools
[58, 59] (Figure 3a). The rationale behind this approach stems from
pretraining an LLM to encode general knowledge, followed by in-domain fine-
tuning of the LLM to encode domain-specific specialist knowledge and aligning
the LLM with human users through role-playing and conversation. Instruction
tuning [60] can be used for the former by training the LLM to follow human
instruction through the use of prompt examples, including dialogues that
incorporate biological reasoning [61]. Additionally, RLHF optimizes LLM
performance by selecting the most human-preferred outputs from a range of
responses to specific prompts, further aligning LLMs with human roles.
Consequently, a single LLM, programmed to fulfill multiple roles, can provide
a more practical and effective solution than developing specialized models. By
assigning specific roles, the agents can replicate the specialized knowledge
of experts across various fields, such as structural biology, genetics, and
chemistry, surpassing the capabilities of querying a non-specialized LLM [62]
and performing tasks previously not possible [63]. Early results in clinical
medicine question-answering suggest that assigning specific roles to GPT-4
[62] can achieve better performance than using domain-specialized LLMs like
BioGPT [64], NYUTron [65], and Med-PaLM [66, 67].

We envision three approaches for assigning roles to biological AI agents:
domain-specific fine-tuning, in-context learning, and automatic generation of
optimized roles. The first approach involves instruction-tuning an LLM across
a large number of biological tasks to ground the LLM in the biological domain,
followed by RLHF to ensure that the tuned LLM performs tasks aligned with the
goals, wants, and needs of scientists. The second approach uses in-context
learning of LLMs [68] to process longer contextual information provided in
inputs, such as biologist-generated instructions, enabling agents to grasp the
domain context for each task. This approach is supported by using textual
prompts to define agent roles [63, 69]. Both strategies require biologists to
carefully gather task-specific data or craft prompts. However, as roles
defined by humans may not always direct agents as intended, there has been a
movement towards allowing LLM-based agents more autonomy in role
specification. This paradigm shift in role definition enables agents to
autonomously generate and refine role prompts, engaging in self-directed
learning and role identification. For instance, Fernando et al. [70]
demonstrate an agent’s ability to evolve and tailor its prompts in reaction to
user inputs. Similarly, Yang et al. [71] investigate the application of LLM as
an optimizer to enhance prompt refinement and optimization for improved
performance in assigned roles. Through this self-referential learning
framework, agents transition from task executors to entities capable of more
autonomous learning.

The agent system, comprising a single LLM prompted to adopt various roles, has
proven to be an effective auxiliary in scientific research. Studies suggest
that agents allocated specific roles exhibit enhanced capabilities compared to
either sequentially querying a single LLM or employing a single tool
repetitively. A case in point is Coscientist [2], which shows the potential of
GPT-4-based agents for chemical research tasks, including optimizing reactions
for palladium-catalyzed cross-couplings. Within Coscientist, GPT-4 undertakes
the role of a planner, serving as a research assistant. The agent uses in-
context prompts to support the use of tools such as web and documentation
search, code execution via Python API, and even symbolic lab language (SLL)
[2]. To complete tasks that require access to a physical device, the planning
agent starts with a prompt provided by the scientist and uses search tools to
compile the documentation for the experiment. Following this, the agent
generates SLL code and executes it, which entails transferring it onto the
device and controlling the device.

###  3.2 Multi-agent AI systems

LLM-based agents implemented through autoregressive LLM approaches acquire
skills such as planning and reasoning by emulating observed behaviors in
training datasets. However, this mimicry-based learning results in limited
agent capabilities, as they do not achieve a deep understanding of these
behaviors [72]. Consequently, a single agent often lacks the comprehensive
skill set needed to complete complex tasks. A practical alternative is
deploying a multi-agent AI system, wherein the task is segmented into more
manageable subtasks. This approach allows individual agents to address
specific subtasks efficiently, even with incomplete capabilities. Distinct
from single-LLM-based agents, a multi-agent system incorporates several agents
endowed with specialized capabilities, tools, and domain-specific knowledge.
For successful task execution, these agents must conform to working protocols.
Such cooperative efforts equip LLMs with unique roles, specialized knowledge
bases, and varied toolsets, simulating an interdisciplinary team of biology
specialists. This approach is akin to the diverse expertise found across
departments within a university or an institute.

In the following, we introduce five collaborative schemes for multi-agent
systems.

Brainstorming agents (Figure 3b). Brainstorming research ideas with multiple
agents constitutes a collaborative session to generate a broad spectrum of
research concepts through the joint expertise of scientists and agents. In
such sessions, agents are prompted to contribute ideas, prioritizing the
volume of contributions over their initial quality to foster creativity and
innovation. This method encourages the proposal of unconventional and novel
ideas, allowing participants to build upon the suggestions of others to
uncover new avenues of inquiry while withholding judgment or critique. The
process enables agents to apply their domain knowledge and resources to form a
collective idea pool. This pool can then be distilled and examined more
thoroughly. To foster divergent thinking and creativity, it can be beneficial
for agents to specialize in areas of biology, for instance, microglia biology,
neuronal degeneration, and neuroinflammation in the case of a multi-agent
system for Alzheimer’s.

Expert consultation agents (Figure 3c). Expert consultation entails soliciting
expertise from individuals or entities with specialized knowledge. This
process involves expert agents gathering information from various sources and
providing insights, solutions, decisions, or evaluations in response. Other
agents or humans then refine their approaches based on this feedback. Liang et
al. [73] illustrate that LLMs can function like human expert reviewers,
offering scientific critiques on research manuscripts. Similarly, an AI agent
might consult another agent specialized in a specific area to refine ideas
within AI systems, mirroring the mentor-mentee dynamics found in academic
environments. In another example, in addressing Alzheimer’s and related
dementias, diagnosing Alzheimer’s based on cognitive criteria might present
borderline cases. Consulting an AI agent could offer additional perspectives,
determining if such cases align with Alzheimer’s based on brain pathology or
alternative biomarkers.

Research debate agents (Figure 3d). In a research debate, two teams of agents
present contrasting perspectives on a research topic, aiming to persuade the
agents of the opposing team. Agents are split into two groups, each adopting
distinct roles for the debate. One group gathers evidence to fortify its
position using various knowledge sources and tools, while the opposing group
critiques this evidence, striving to expose or neutralize its weaknesses with
superior evidence. The objective for each faction is to articulate their
arguments more effectively than their rivals, engaging in a systematic
discourse to defend their viewpoint and challenge the veracity of their
adversaries’ assertions. This methodology promotes critical thinking and
bolsters effective communication as each team endeavors to construct the most
compelling argument supporting their stance.

Round table discussion agents (Figure 3e). Round-table discussions involve
multiple agents engaging in a process that fosters the expression of diverse
viewpoints to make collaborative decisions on the topics under discussion. In
such sessions, agents articulate their ideas and insights, pose questions, and
provide feedback on others’ contributions. They then respond to these queries,
refine their initial propositions based on feedback, or attempt to persuade
their peers. This method promotes equal participation among all agents, urging
them to contribute their expertise and perspectives, offer constructive
criticism, question underlying assumptions, and suggest amendments to improve
the proposed solutions. For instance, Reconcile [74] implements a
collaborative reasoning scheme among LLM agents through successive rounds of
dialogue. Agents attempt to convince each other to adjust their responses and
use a confidence-weighted voting mechanism to achieve a more accurate
consensus than if a single LLM-based agent is used. During each discussion
round, Reconcile orchestrates the interaction between agents using a
‘discussion prompt,’ which includes grouped answers and explanations produced
by each agent in the preceding round, their confidence levels, and examples of
human explanations for correcting answers.

Self-driving lab agents (Figure 3f). The self-driving laboratory is a multi-
agent system where the end-to-end discovery workflow is iteratively optimized
under the broad direction of scientists but without requiring step-by-step
human oversight [75]. Once the agent system is trained, it can describe
experiments necessary to test the generated hypotheses, analyze the results of
said experiments, and use them to improve its internal scientific knowledge
models. Agents in the self-driving system need to address the following three
elements: determine inductive biases to reduce the search space of hypotheses,
implement methods to rank order hypotheses considering their potential
biomedical value with experimental cost, characterize skepticism via
uncertainty quantification and analysis of experiments in reference to the
original hypothesis, and refine hypotheses using data and counterexamples from
experiments [76]. Ideally, hypothesis agents are creative and skeptical when
developing biological hypotheses that extrapolate indirectly from the existing
body of knowledge [17]. Experimental agents steer operational agents that use
a combination of in silico approaches and physical platforms to execute
experiments. Following this, reasoning agents integrate the latest results to
guide future experimental design. The utility of experimental results, such as
the yield of high-throughput screening of a chemical library against a
biological target, can be compared for different versions of the agent system
given a time budget for hypothesis and experiment generation.

##  4 Levels of autonomy in AI agents

AI agents, when integrated with experimental platforms, can operate at varying
levels of autonomy tailored to the diverse requirements across biomedical
fields. We classify these AI agents into four levels according to their
proficiency in three areas of discovery: Hypothesis, Experiment, and Reasoning
(Table 1). Specific capabilities within each area define these levels,
necessitating that agents exhibit the capabilities for a given level across
all areas (an agent with Level 3 capabilities in the Experiment area but only
Level 2 capabilities in Reasoning and Hypothesis areas would be classified as
Level 2).

Level 0, denoted as no AI agent, using ML models as tools. This level aligns
with the prevailing approach of using interactive and foundation learning
models (Section 2). At this level, ML models do not independently formulate
testable and falsifiable statements [77] as hypotheses. Instead, model outputs
help scientists to form precise hypotheses. For example, a study employed
AlphaFold-Multimer to predict interactions of DONSON, a protein with limited
understanding, leading to a hypothesis about its functions [78]. Level 1,
termed AI agent as a research assistant, features scientists setting
hypotheses, specifying necessary tasks to achieve objectives, and assigning
specific functions to agents. These agents work with a restricted range of
tools and multi-modal data to execute these tasks. For instance, ChemCrow [3]
combines chain-of-thought reasoning [79] with ML tools to support tasks in
organic chemistry, identifying and summarizing literature to inform
experiments. In another example, AutoBa [80] automates multi-omic analyses.
These two agents are designed for narrow scientific domains; ChemCrow and
AutoBa optimize and execute actions to complete tasks that are designed and
predefined by scientists. Level 1 agents [81, 82, 80, 3] formulate simple
hypotheses inferred from existing knowledge and utilize a limited set of
tools, lacking the capacity for a major scientific discovery necessary to
achieve Level 2 autonomy.

At Level 2, AI agent as a collaborator, the role of AI expands as scientists
and agents collaboratively refine hypotheses. Agents undertake tasks critical
for hypothesis testing, using a wider array of ML and experimental tools for
scientific discovery. However, their capability to understand scientific
phenomena and generate innovative hypotheses remains constrained, highlighting
a linear progression from existing studies. The transition to Level 3, or AI
agent as a scientist, marks a major evolution, with agents capable of
developing and extrapolating hypotheses beyond the scope of prior research,
synthesizing concepts beyond summarizing findings, and establishing concise,
informative and clear conceptual links between findings that cannot be
inferred from literature alone, eventually yielding a new scientific
understanding. While multiple Level 1 agents exist across various scientific
fields, Levels 2 and 3 agents have yet to be realized.

The levels of autonomy described for artificial general intelligence (AGI)
agents in scientific contexts, particularly in biology, deviate from existing
taxonomies that focus on general human-AI interaction separate from the
collaborative dynamics between scientists and AI. Existing taxonomies of
autonomy consider solely the balance of responsibilities between AI agents and
humans—with no consideration of biomedical discovery—and focus on developing
AGI to surpass human performance across varying skill levels [83].

As the level of autonomy increases, so does the potential for misuse and the
risk of scientists developing an overreliance on agents. While agents have the
potential to enhance scientific integrity, there are concerns regarding their
use in identifying hazardous substances or controlled substances [84].
Responsible development of agents requires developing preventive measures;
further discussion on risks is in [85, 86] and Section 6. The responsible
deployment of agents must account for the risk of overreliance, particularly
in light of evidence that LLMs can produce convincing but misleading claims
and spread misinformation. The risks will likely increase as agents undertake
more autonomous research activities. Agents must be subjected to the same
scrutiny as scientists, including reproducibility and rigorous peer review of
agentic research.

###  4.1 Illustration of AI agents in genetics

Research in human genetics seeks to understand the impact of DNA sequence
variation on human traits. LLM-based agents operating at Level 1 would perform
specific tasks relevant to genetic studies. For instance, in a genome-wide
association study (GWAS), a Level 1 agent can write bioinformatics code to
process raw genotype data to (1) execute quality control measures, such as the
removal of single-nucleotide polymorphisms (SNPs) missing in many individuals
or control for population stratification [87], (2) estimate ungenotyped SNPs
through imputation, and (3) conduct the appropriate statistical analyses to
identify relevant SNPs, taking into account the false discovery rate [88].
Following the analysis, the Level 1 agent reviews and reports findings,
including any filtered SNPs and rationales for their exclusion.

Instead of executing narrow tasks following human instruction, a Level 2 agent
identifies and then executes tasks on its own in order to refine a hypothesis
initially given by the scientist. For example, it may explore the
effectiveness of drugs for a patient subgroup within complex diseases, where
genetic underpinnings can influence drug response [89]. Given a hypothesis
that a particular drug is effective in a subset of patients with idiopathic or
genetic generalized epilepsy (GGE)—a condition with a robust genetic causality
[90]—a Level 2 agent would synthesize genetic information from GWAS meta-
analyses [91], such as the UK Biobank [92], targeted sequencing studies [93],
and knowledge bases like Genes4Epilepsy [94]. The agent identifies GGE
subtypes and causal genes by analyzing patient genetic data, predicting which
subgroups might benefit from the drug based on genetic markers. It would then
conduct in vitro functional studies to confirm these predictions, ultimately
presenting evidence on how the drug could benefit GGE patient subpopulations
by synthesizing concepts beyond summarizing findings.

Level 3 agents coordinate a system of agents (Figure 3) to discover and
evaluate gene markers for specific phenotypes. These agents help initiate new
study groups and optimize non-invasive methods of DNA collection for cost-
effectiveness and recruitment processes [95]. Once data are collected, the
agents innovate statistical methods to identify causal variants from genotypic
data amidst confounders such as linkage disequilibrium [96] and develop in
vitro techniques for validating candidate gene markers in disease models.
Level 3 agents collaborate with scientists to generate and test hypotheses for
comprehensive genetic insights.

###  4.2 Illustration of AI agents in cell biology

Cells are fundamental units of study in cell biology. Advances in single-cell
omics, super-resolution microscopy, and gene editing have generated datasets
on normal and perturbed cells, covering areas such as multi-omics [97, 98,
99], cell viability [100], morphology [101, 102], cryo-electron microscopy and
tomography [103, 104], and multiplexed spatial proteomics [105, 106]. This
proliferation of data has spurred interest in in silico cell modeling [107]
with the overall goal of identifying new mechanistic understanding and
adaptive cellular function.

ML tools have been instrumental in analyzing data across these cellular
modalities, albeit as Level 0 agents these tools lack autonomous research
capabilities. At Level 1, agents integrate specialized Level 0 models designed
for narrow tasks such as cell type annotation to assist in hypothesis testing
and answering scientific questions. These agents actively engage in research,
synthesizing literature and predicting cellular responses using integrated
models. For example, to investigate a new compound’s mechanism of action (MoA)
[99], a Level 1 agent predicts its effects in various cellular contexts [108].
These predictions inform experimental design, such as transcriptomic and
proteomic screening. The agent also retrieves and refines experimental
protocols for execution on platforms [109]. After data generation, the agent
applies predefined bioinformatics pipelines to extract information from multi-
modal data before scientists interpret the results.

Level 2 agents not only execute predefined tasks but also generate hypotheses
on cellular functions and compound mechanisms. They autonomously refine tasks
to support scientific reasoning, enabling more efficient exploration of
complex phenotypes like drug resistance through combinatorial perturbations
[110]. By managing the experimental cycle and continuously updating their in
silico tools, Level 2 agents aim to minimize experimental redundancy and focus
on key variables of resistance. However, efficient probing of the intractable
combinatorial space of genes and contexts is still a considerable challenge.
In light of this, level 2 agents would propose and optimize experiments for a
comprehensive yet cost-effective evaluation of hypotheses through knowledge
bases, managing the entire experimental cycle from protocol retrieval and
improvement to execution, based on iterative feedback from scientists [56].
Automatic data analysis occurs at every experimental stage, with the agent
refining hypotheses, adjusting plans, and continuously updating its ML tools
based on a synthesis of predictive content, uncertainty, and newly acquired
data.

Level 3 agent systems will implement hybrid cell models as digital-
experimental simulators of cellular responses to any type of perturbation.
Agents in the hybrid cell model system combine AI tools (digital agents) with
high-throughput platforms (experimental agents). Digital agents, such as LLM-
based agents, autonomously identify knowledge gaps through literature
synthesis and handle any perturbagen (extrinsic events such as gene knockouts,
compounds, cell-cell interactions; intrinsic events such as cell cycle) based
on criteria such as data volume, biological relevance, and clinical needs.
Experimental agents generate high-throughput transcriptomics [111] and
proteomics measurements [112, 113] with a spatial resolution for
perturbations, perturbation screens including in vivo sequencing, and mass
spectrometry platforms [113], as well as barcoded spatial proteomics [112,
114]. Optimization of experimental protocols [115, 101, 116] shifts the role
of scientists from performing minute operational tasks to managing hybrid cell
models.

###  4.3 Illustration of AI agents in chemical biology

A major focus of chemical biology scientists is on molecular interactions
within cells to manipulate biological systems at molecular and cellular
levels. An ideal AI agent would be able to analyze any molecular interaction,
help design new drugs, and provide more valuable chemical probes for
biological systems.

Despite considerable advances in applying ML to chemical biology, progress
toward an AI agent proficient in the scientific method within this field
remains limited. Existing predictive models for protein structure prediction
[39], molecular docking [117], and generative models for protein design [118,
119] are ML tools that can be wrapped within agents through ‘function calling’
and APIs (Section 5.2). Such agents would be categorized as Level 0, where a
scientist oversees all activities. A Level 1 agent, capable of studying a
specific protein target, integrates ML tools, such as AlphaFold for structure
prediction [39] and neural networks for screening chemical libraries [120] to
find candidate chemical compounds that might bind the target. This agent would
possess elementary reasoning abilities by probing relevant literature and
designing experiments within a specific domain [2], although it would require
human guidance for complex inquiries and error correction. The scientist will
need to guide the agent to assist with more complex questions and for error
checking. For example, a Level 1 agent tasked with binder design for a well-
known target would design very similar drug derivatives to existing binders,
for which the scientist would have to guide the agent with many different drug
scaffolds. However, for a more challenging target, it would provide
nonsensical binders.

A Level 2 agent would surpass Level 1 by identifying novel research avenues,
such as designing new binders by leveraging trends in related targets; a
method demonstrated in materials science for discovering new materials [121].
This agent would employ advanced reasoning extracting literature with more
abstract connections to the research task, such as identifying scaffolds that
bind to similar pockets and adapting them for the target. In addition, the
agent has broader capabilities in planning and designing experiments than a
Level 1 agent with expertise in more domains such as retro-synthesis,
crystallography, bioassays, and directing robotic arms to execute these
experiments. It can also reason from the experiments’ results to identify
novel discoveries and propose follow-up experiments. Thus, the agent
demonstrates a more robust understanding of chemical biology across
hypotheses, experiments, and reasoning. The scientist collaborates with the
agent by providing feedback on the quality of designed inhibitors and guiding
the agent’s decisions.

A Level 3 agent is capable of studying all types of molecular interactions in
a cell. This agent would work alongside human scientists to explore research
questions, proposing de novo binders for an undruggable target [122] or a
poorly studied target. Unlike the Level 2 agent’s use of well-established
protocols, a Level 3 agent unlocks experimental capabilities at spatial and
temporal scales that are not currently accessible to experimentation, or
design of in situ experiments to study molecular interactions. We can envision
the agent proposing candidate molecules, synthesizing molecules with more
complex pathways, and designing and executing assays to test efficacy.
Finally, it contextualizes results with literature to understand what chemical
interactions would occur and why they would be prevalent.

##  5 Roadmap for building AI agents

AI agents can be obtained through LLM-based systems augmented with different
modules [4, 58, 59] that implement functionalities and accomplish tasks
detailed in Sections 3 and 4. Here, we describe these modules (Figure 4),
focusing on perception, interaction, memory, and reasoning that are necessary
for AI agents to engage with human and experimental environments and to make
decisions. Interactions between an agent and its environment are characterized
by two elements: the agent’s perception of its surroundings and its subsequent
engagement with them. Perception modules enable the agent to interpret and
assimilate information from various data modalities. Then, learning and memory
allow agents to interact with an environment and complete tasks, by acquiring
new knowledge and retrieving previously learned one. Finally, Reasoning
modules process information and execute action plans. Figure 5e illustrates an
agent system that uses perception, interaction, memory, and reasoning modules
for research in cell biology [123].

###  5.1 Perception modules

Perception modules equip LLM-based agents with the capability to understand
and interact with elements in the environment in which they operate, such as
biological workflows and human users. For perception, agents need to integrate
abilities to receive feedback from multiple sources: scientists [50], the
environment [63], and other AI agents [14, 124]. This requires accommodating a
diverse array of modalities. These include: text descriptions [7]; images from
light and (cryo-)electron microscopy to assess cellular processes across many
conditions simultaneously [103, 104, 125]; videos from live imaging to assess
developmental processes or animal behaviors across time [126, 127, 128];
longitudinal biosensor readouts and genomics profiles of cells [129]; mass
spectrometry-based proteomics to decipher protein homeostasis [130, 25]; and
miniaturized platforms for conducting biochemical assays and 3D culture
systems that mimic the physiological context of organ systems [109].

AI agents can take different approaches to interacting with environments. The
most direct one involves using natural language, which represents a common
perception modality for LLM-based agents. Other techniques involve using
multi-modal perception modules, where agents process multi-modal data streams
from the environment or align multi-modal inputs with text-based LLMs.

Conversational modules. With the rise of ChatGPT, the ability of AI agents to
interpret natural language has reached such a high level [50] that it is now
possible to build interfaces to agent systems that are entirely based on
natural language with limited misinterpretations. The main focus is chat
interfaces that preserve conversational history in a scrolling window, where
users can converse with agents in a manner that resembles the standard
approach of written human-to-human interaction. This approach allows
scientists to express their queries using their language, promoting initiative
and enabling them to precisely describe what they want. We envision that
agents will maintain a history of interaction with scientists using natural
language, which, in turn, will allow us to keep track of scientific
interactions with agents [63, 69]. Combining the history trace of these
interactions with retrieval-augmented generation (RAG), it will be possible to
develop personalized discovery workflows tailored to individual scientists.

Multi-modal perception modules. Agents align LLMs with other data types to
consider data modalities beyond natural language. This approach helps agents
better model the changing environment in which the agent acts and dynamically
adjust its outputs to new situations, such as evolved biological states in a
virtual cell model. The alignment process involves two main strategies:
textual translation and representation alignment. Textual translation converts
inputs into a textual format, such as transforming data from robotics into
textual descriptions that log environmental states [10]. Alternatively,
through representation alignment, data from different modalities are analyzed
by modality-specific models to generate representations, such as using the
visual encoder from CLIP [131] for visual information processing. These
representations are then aligned with LLM textual representations [132, 133,
134, 125, 135] through instruction tuning [125], enabling agents powered by
LLMs to perceive and interpret multi-modal data. An alternative to alignment
involves allowing the agents to receive input expressed in different
modalities [8, 136]. For instance, Fuyu [136] uses a decoder-only transformer
architecture to process image patches and text tokens jointly. Similarly,
Gemini [8] is engineered to handle visual, audio, and text inputs within a
single model. Once perception modules are implemented for agents to receive
inputs from the environment, modules for interaction (Section 5.2) and
reasoning (Section 5.4) follow to process the inputs and interact externally.

###  5.2 Interaction modules

Beyond conversational modules, in biological research, scientists use ML-based
and other tools, explore datasets through graphical user interfaces (GUIs) to
analyze and visualize data, and engage with physical equipment and wet lab
experimental platforms. Chat-optimized LLM-based agents thus need interaction
capabilities to communicate and collaborate with scientists, other AI agents,
and tools to function beyond a simple chatbot. Agents must incorporate
essential interaction modules to interact with elements in the environment.
These include agent-human interaction to support communication with scientists
and following human instruction [137, 138], multi-agent interaction for
collaboration among agents, and tool-use action to access ML tools and
experimental platforms.

These interaction abilities of LLMs, when combined with interactive ‘function
calling’ (i.e., LLM requesting for tasks to be completed), can act as an
intermediary between scientists and the agent’s interface, as well as between
scientists and various functional items (e.g., tools, other agents, humans).
This allows scientists not to search where and how to accomplish tasks but to
simply express their intentions in their language. At the same time, the
advantages of functional items are fully preserved because agents can interact
with tools and use them to provide feedback.

Agent-human interaction modules. The interaction between scientists and AI
agents synchronizes scientific objectives with AI agents through cooperative
communication and modeling of biological knowledge. Natural language
processing and human evaluation methods are predominantly used to develop this
interaction capability. InstructGPT [50] enhances the GPT model through
supervised fine-tuning with examples of human dialogues to improve the model’s
conversational skills. The alignment between agents and humans can be refined
through RLHF, which adjusts the model based on a reward model trained using
human assessments of the model’s responses. Alternatively, RLHF can be
replaced by direct preference optimization [139], which is a parameterized
method that provides a more consistent and efficient alignment with human
preferences. Through agent-human interaction, agents become attuned to human
needs and preferences [138, 11], using human insight as a directive for
carrying out complex tasks [14]. For instance, Inner Monologue [138] employs
human feedback to discern user preferences or interpret ambiguous requests in
an embodied context. In AutoGPT [11], humans formulate tasks and score
solutions returned by agents, and AutoGen [14] can use human expertise to
solve tasks better than agents alone.

Multi-agent interaction. Multi-agent interactions support solving complex
goals that agents could not complete if they operated independently of each
other. In such interdisciplinary systems, agents that could specialize in
different biological domains, each with distinct capabilities, engage in
interactions through various communication means. Language has emerged as the
predominant medium for multi-agent interactions due to the ability of agents
to communicate with humans linguistically [5, 140, 14, 124, 74]. An instance
of this is Generative agents [63], which create interactive environments where
agents mimic human behavior and interact using natural language. Different
strategies are used for multi-agent interaction, including cooperation [141,
142, 143] and negotiation [74, 144, 145]. For example, MetaGPT [142] applies
standardized operating procedures from human teamwork to define tasks and
agent responsibilities.

Through these approaches, agent interactions make it possible to tackle tasks
that are too complex for just one agent to handle [146, 81]. MedAgent [81]
leverages the expertise of multiple medical AI agents for medical reasoning.
Similarly, RoCo [146] employs robot agents with varied roles to accomplish
complex tasks in the physical world. Multi-agent interaction can also boost
the proficiency of less skilled agents by allowing them to learn from more
experienced counterparts [147]. These interactions also enable the creation of
simulations for a variety of environments, ranging from public health
scenarios [148] to human social behaviors [63, 149], enhancing the system’s
adaptability and application in diverse contexts.

Tool use. To manage tasks from diverse environments, agents require tools to
boost their capabilities [150]. Commonly used tools are application APIs
[151], search engines [152], ML models [153], knowledge databases [154], and
robotic machinery for physical tasks [155, 156, 10]. Different Level 1 agent
systems have been developed that can interact with one or more types of tools.
ChemCrow [3] leverages chemical tools and search engines to address chemical
challenges. WebGPT [152] can conduct searches and navigate web browsing
environments. SayCan [156] controls a robot in the physical world using an LLM
to complete tasks. To invoke these tools, AI agents generate commands in
specific formats [153, 151, 154] or query pre-trained control models to
execute actions [156, 157]. To develop these capabilities, agents can use in-
context learning [153] or fine-tuning with tool-use demonstrations [151],
where the latter represents a more sophisticated approach.

In the case of in-context learning, it is necessary to include system
abilities in the prompt so agent systems can use ‘function calling’ to query
tools. For example, HuggingGPT [153] uses ChatGPT as a controller to integrate
all ML models on Hugging Face through in-context learning. The alternative
approach consists of using model fine-tuning with ‘function calling’ to create
an LLM-based agent with integrated abilities of a function/tool. For instance,
Toolformer [151] introduces a self-supervised learning method to master the
use of tools’ APIs with minimal demonstrations for each API.

By modeling scientists’ needs by analyzing natural language textual inputs, AI
agents can select the most likely available tool, identify the desired user
interface component, and execute the scientist’s expected actions. Interaction
modules are designed to be integrated and adapted to suit changing
environments. For Level 2 and Level 3 agents, agents autonomously learn new
types of interactions and how/when to start using new tools.

###  5.3 Memory and learning modules

When using tools and ML models for biological research, scientists keep
records of experimental logs and plan their next steps based on them. In AI
agents, memory modules alleviate the need for manual log recording by
memorizing necessary experimental outputs. Contrary to ML models that perform
one-time inference to generate predictions, memory modules in LLM-based agents
store and recall information. This is necessary for executing complex tasks
and adapting to new or evolving environments. Memory modules are designed to
store long-term and short-term learned knowledge. As agents encounter new
situations and acquire data, memory modules get updated with new information.

Long-term memory modules. Long-term memory stores essential and factual
knowledge that underpins agent behavior and understanding of the world,
ensuring this information persists beyond task completion. This memory can be
internal, encoded within the model’s weights via learning processes [9, 158],
or external, maintained in auxiliary knowledge bases [159, 160]. Internal
memory is directly used for accomplishing zero-shot tasks [7, 8] while
accessing external memory requires actions by the agent to fetch and integrate
data into short-term memory for immediate use [161, 162]. For instance, ChatDB
[154] uses an external database for memory storage, and MemoryBank [163]
encodes memory segments into embeddings for later retrieval. Agents can query
knowledge banks, such as a GWAS database to find genetic evidence for a
candidate protein target, a knowledge base of therapeutic mechanisms of
action, and scientific literature with up-to-date information for the agent to
integrate and decide whether the protein can be modulated through a
therapeutic perturbation (Figure 5b). The learning process updates long-term
memory by adding new knowledge or replacing outdated information. Internal
memory of an agent can be updated using parameter-efficient fine-tuning [158,
164], interactive learning [50], and model editing [165]. These strategies
must be effective for large models [164] and avoid the loss of previously
learned information [166]. On the other hand, updating external memory is more
straightforward, involving modifications to the knowledge base [154, 163]. For
example, in drug discovery, updating long-term memory by adding a new compound
in development to the drug bank is a convenient way to maintain an up-to-date
agent.

Short-term memory modules. AI agents use short-term memory to temporarily
store information during their interactions. This short-term memory is enabled
through in-context learning, where relevant information is integrated as
context prompts [167, 156] or via latent embeddings [132, 125] in LLMs. For
chatbots, previous conversations are kept as text prompts, supporting multiple
rounds of dialogue [50, 168]. The text-based approach lays the groundwork for
communication in multi-agent [74, 145] and agent-human scenarios [11, 14]. In
embodied AI agents, environmental feedback [156, 167] is captured in textual
format, acting as a short-term memory that aids reasoning. Following
perception, multi-modal inputs are converted into latent embeddings, which
function as short-term memory. LLaVA [125] uses latent embeddings generated by
visual encoders to retain visual information. Short-term memory allows agents
to temporarily acquire skills, such as tool usage [153, 151], store
information about recent states of a biological system [168, 167], and keep
track of outcomes from earlier reasoning efforts [12]. This learning mechanism
is crucial for agents to learn and apply new knowledge under new conditions.
Moreover, short-term memory can temporarily override long-term memory,
allowing agents to precede recent information over older knowledge within
their model weights [169]. Agents can be informed by past experiences stored
in their short-term memory to tell which experiments to run in the future. In
Figure 5a, we detail an example where the agent recalls experiments for a
homologous protein to inform the initial inhibitor design for the given
protein.

###  5.4 Reasoning modules

Biological research involves a multidisciplinary and multistage process that
integrates the expertise of scientists from various disciplines. These
scientists formulate hypotheses, design experimental setups based on these
hypotheses, interpret the results, and plan the next steps. Central to this
process is human reasoning, an ability that conventional ML models find
challenging to replicate. The integration of reasoning capabilities in AI
agents holds the potential to expedite biological research by assisting in
several of these critical steps. Reasoning improves agents’ capabilities to
plan experiments, make decisions on biological hypotheses, and re-solve
competing candidate biological mechanisms. Reasoning modules can be
implemented using direct prompting [170] and few-shot in-context learning
[79]. Additionally, agents can use planner models [171, 172] and action models
[167]. We classify reasoning modules into two categories: direct reasoning and
reasoning with feedback. The classification depends on whether the agent
adjusts its plan in response to experimental or human feedback.

Direct reasoning modules. In direct reasoning, an agent performs planning and
reasoning based on the current state of the environment, which can follow
different reasoning patterns, such as single-path and multi-path reasoning.
Single-path reasoning involves the agent breaking down the task into multiple
recursive steps [173]. For instance, chain-of-thought (CoT) reasoning allows
agents to reason step-by-step either by using in-context examples [79] or by
applying a zero-shot prompt like "Let’s think step-by-step” [170]. Leap-of-
thought [174] encourages the model to use creative rather than logical
reasoning. Although single-path reasoning matches well with certain situations
[175], its ability to adjust to different conditions is limited.

Conversely, multi-path reasoning examines several paths before consolidating
them into a final plan [176, 177], allowing for a more thorough planning
process that accounts for different scenarios. For example, Least-to-Most
prompting [178] breaks down tasks into subproblems solved sequentially. Self-
consistent CoT [179] chooses the most consistent answer from a set of CoT
answers. Tree-of-thoughts [176] extends reasoning paths into a tree-like
structure, generating multiple paths from each thought node and using search
algorithms to select the final path. Graph-of-thoughts [180] further develops
reasoning paths into a graph structure for complex reasoning. To identify the
optimal path, methods such as voting strategies [179], Monte Carlo tree search
[181], and breadth/depth-first search algorithms [176] are used. Through
direct reasoning, agents can generate multiple threads of thought that could
consider the best pathways, protein targets, and experiments that can be run
to test the role of a candidate protein target (Figure 5c).

Reasoning with feedback. Experimental and human feedback can help AI agents to
improve reasoning and planning processes [12, 69, 161]. This feedback may
include agent-human interaction (Section 5.2) and responses from agents, which
can be complementary biological assays quantifying downstream effects of
target molecules [182]. In each reasoning cycle, React [12] incorporates
insights from previous actions to refine its thought process and inform future
actions. LLM-Planner [183] dynamically adjusts plans based on new observations
in an embodied environment. Inner Monologue [138] uses both passive and active
scene descriptions and feedback from recent actions to guide future actions.
Voyager [69] improves planning for subsequent steps by considering environment
feedback, execution errors, and self-verification.

Beyond external feedback, an agent’s feedback mechanism enables self-assessing
the initial plan [182, 184]. Techniques like self-refine [182] revise action
outputs based on the LLM evaluation, the self-check [182] mechanism allows the
agent to review and adjust its reasoning, and reflection [13] mechanisms use
prompt agents to update their decision-making. These techniques incorporate
feedback from biologists, such as exploring experimental methods and
environmental constraints like lab inventory (Figure 5d).

##  6 Challenges

The perspective outlines key steps to implement AI agents in biomedical
research and highlights areas that can benefit from agentic AI. Challenges
remain and may, in some cases, be amplified when multi-agent systems become
available. Below are technical obstacles and challenges to overcome (Figure
6).

###  6.1 Robustness and reliability

A barrier facing the deployment of agent systems – specifically those
categorized within Levels 2 and 3 as discussed in Section 2 – is their
propensity for generating unreliable predictions, including the hallucination
of non-factual information, reasoning errors, systematic biases, and failures
in planning when connected with tools and experimental platforms. These issues
can be exacerbated by overconfidence in such flawed predictions (agents are
unaware of their knowledge gaps) and high sensitivity to the precise
formulation of queries, particularly in the context of LLM-based agents.

Such behavior has been traced to the pretraining phase of these models. By
contrasting the predicted word sequence and the sequence present in the
training data, the autoregressive loss function influences the subsequent
model performance with the probability distribution of its inputs, the
sequence of generated outputs, and the frequency of tasks it encounters during
training [185]. As a result, model performance degrades on task variants that
deviate from the assumptions made during training [186].

Sensitivity to input and task probability also offers a potential explanation
for the widely observed success of various prompting techniques [79, 187, 176]
(methods to paraphrase the same query). By providing informative context,
instructive reasoning steps, or representative examples, these techniques can
act as an empirical means by which task and input probability (and, thus,
model performance) are increased. However, crafting high-quality prompts tends
to be highly empirical while requiring significant effort and domain
knowledge.

Beyond the linguistic domain, even the most advanced models fail in tasks with
real-world entities that require physically meaningful actions, posing an
obstacle to embodied agents. While embedding continuous sensor data into a
language model can lead to improvements [129], limitations to understanding
physical interactions and long-horizon planning remain. The complexities of
training such multi-modal systems, the need for large datasets to cover the
range of embodied tasks and environments, and the computational demands of
processing multi-modal inputs all remain open questions [8]. Deployment faces
challenges from false negatives causing repeated attempts and eventual
stalling of the embodied agent [138]. Hence, it is necessary to verify the
agent action plan before execution.

Uncertainty quantification can trigger fall-back safety measures like early
termination, pre-defined safe maneuvers, or human-in-the-loop interventions.
However, foundation models cannot reason about the uncertainty associated with
their outputs, and no well-established statistical protocol exists for
increasingly ubiquitous architectures [188, 48]. Techniques such as various
forms of prompting, _e.g._ , [189, 179, 190] estimate uncertainty based on the
model’s predictive distribution, p(output|input), which may itself be subject
to bias ([185], Section 3.3); furthermore, it does not consider the
distribution of model parameters consistent with the observed training data
and marginalizes over its predictions [191]. While conformal prediction [192]
has emerged as a framework for uncertainty estimation of model predictions,
its sensitivity to the choice of underlying statistical assumptions and the
calibration of confidence levels have been criticized. The lack of a default
technique is partly due to the difficulty of establishing a thorough quality
assessment of uncertainty estimates. This makes it difficult to make choices
in agent design and to reassure users about its calibration.

An overarching concern is that advanced capabilities come at the cost of
compromised transparency and the risk of misalignment. For instance,
integrating human feedback can promote desirable agent behavior, but it can
also exacerbate persuasive abilities, echoing false beliefs [193].
Furthermore, fine-tuning existing models with new data can compromise their
original alignment, challenging the integrity of the AI agent’s intended
purpose [194]. Jailbreak attacks can similarly affect post-deployment,
highlighting the need for rigorous evaluation [195].

###  6.2 Evaluation protocols

With more AI agents being developed, frameworks for biologists and lay user
evaluations need to assess axes of agent performance beyond accuracy.
Evaluating AI agents requires an analysis of their theoretical capabilities
and an assessment of practical implications, including ethical considerations,
regulatory compliance, and the ability to integrate into discovery workflows.
The challenge lies in developing evaluations that consider these diverse
factors. Agents that integrate ML tools, particularly those developed by
corporations, may undergo updates without prior notice to users. This poses
challenges for reproducibility, as updates may alter the model’s behavior or
performance without researchers being aware. The scientific community requires
transparent change logs and version control for agents, akin to practice in
software development.

Evaluation frameworks consider either a holistic evaluation of LLMs [196, 197]
or weak spots such as task framing [198, 199], long temporal dependencies,
invalid formatting or refusal to follow instructions [200]. A caveat of such
methods is the risk of evaluating how well the agents have learned to use
specific APIs versus general results grounded in real-world interaction.
Another challenge in evaluating agents is that biological systems are
inherently dynamic, characterized by non-stationary distributions that evolve
due to genetic mutations, environmental changes, and evolutionary pressures.
Agents trained on static datasets may struggle to accurately model or predict
outcomes in these changing systems. The challenge lies in developing agents
capable of adapting to or continuously learning from new data, ensuring their
predictions remain accurate as the underlying biological systems change.
Techniques such as online learning, transfer learning, and reinforcement
learning can be used to address this issue, but they come with their own set
of challenges related to data availability and model complexity.

###  6.3 Dataset generation

As laid out, the vision of advanced agents in biology requires the capability
of seeking, aggregating, perceiving, and reasoning over data from various
modalities, created using differing specifications and with inherent variation
in quality and volume. To support this vision, there is a critical need for
large, open datasets that are both comprehensive and accessible, enabling the
development of models across biological applications. Much human effort in
building complex systems for biology is dedicated to gathering and preparing
such data for use in ML models (e.g., specific to a particular modality, such
as graphs, time series, or discrete sequences [201]). This requires vetting
processes and clear criteria for assessing the reliability and applicability
of datasets.

Noisy data, characterized by errors, inconsistencies, and outliers, poses a
significant challenge for models attempting to extract meaningful patterns and
insights with minimal human oversight or data preparation effort. In addition,
multi-modal data requires models to process different data representations and
formats and bridge semantic gaps between them. Tackling these challenges
necessitates advanced feature extraction, fusion, and noise mitigation
techniques while maintaining robustness. As no pretraining phase (no matter
how extensive) will be able to provide adequate examples from all data
sources, models will also have to generalize to previously unseen sensory
inputs.

###  6.4 Governance of AI agents

The governance of AI agents presents challenges that intersect technological,
scientific, ethical, and regulatory domains. One challenge is establishing
comprehensive governance frameworks that balance innovation with
accountability [202]. As AI agents gain autonomy, the necessity for robust
guidelines to ensure responsible development, deployment, and
commercialization grows. The discourse increasingly advocates for agent
safeguarding to take precedence over further advancements in autonomy. Yet,
navigating the regulatory landscape and forging an international consensus on
AI governance remains complex while the advancement of agent capabilities
continues. Striking a balance between innovation and safeguarding against
potential risks requires collaboration among industry leaders, scientists, and
policymakers [203].

Safe adoption of AI agents requires addressing concerns of safe deployment.
Aligning ML tools, such as LLMs, with ethical standards remains an open
challenge, and ensuring the alignment of the agent as a digital entity raises
complexity. Guidelines concerning human-agent interactions are underdeveloped
despite the potential for both unintended harmful consequences and malicious
intent. Tang et al. [85] describe a safeguarding framework that includes
training, licensing, and mandatory safety and ethical compliance checks for
agents.

As AI agents become more integral to workflows in biological domains,
monitoring their behavior grows increasingly complex. Currently, verifying the
accuracy and trustworthiness of agent outputs is not straightforward, with
only a limited number of systems capable of linking generated content to
relevant references. Assessing the synthesized knowledge may be impractical
and unattainable as agents evolve further. When agents’ capabilities become
comparable to those of human experts, the risk of becoming overly reliant on
AI increases, which could lead to a decrease in human expertise. In the worst-
case scenario, such reliance could introduce a broad spectrum of safety
hazards due to inadequate oversight.

###  6.5 Risks and safeguards

Developing autonomous experiments and deployment that do not include careful
planning, broad consultation, competent execution, and ongoing adaptation
might create long-term harms that outweigh the benefits. Although anticipating
all potential complications is impossible, exploring possible problems early
and frequently could reduce the expected cost of such issues. The space of
ethical considerations relevant to AI agents is too broad to canvass
comprehensively here, but this section highlights a few key categories.

Neglect can lead to risks similar to those of malicious intent. Multi-agent
systems where some agents represent LLMs might, through equipment malfunctions
and insufficient maintenance, inadvertently create harmful substances, for
instance, by contaminating a procedure that would otherwise be safe. This
issue is not unique to multi-agent systems; instead, it is a general lab
safety concern. However, the absence of close human supervision removes a
critical auditing layer. The increased role of automation in agent systems
raises safety issues: a powerful, unaligned system prone to misinterpreting
user requests or unfamiliar with lab safety practices could, given access to a
well-stocked scientific facility, do damage by, for instance, mixing volatile
substances or developing and dispersing toxins or pathogens. These are among
the scenarios that most concern AI safety researchers.

LLM agents leverage the world knowledge and general reasoning abilities of
LLMs obtained during pretraining for solving robotics and planning tasks.
However, while considerable effort has been made to teach the robots the
“dos,” the “don’ts” received less attention. In agent systems, teaching the
robots the “don’ts” is crucial: conveying instructions about prohibited
actions, assessing the robot’s comprehension of these restrictions, and
ensuring compliance [204]. For LLM agents, Yang et al. [204] developed a plug-
in safety chip, a queryable safety constraint module that translates natural
language constraints into formal safety constraints that the robot adheres to.
Experiments with robots highlight the potential for integrating formal methods
with LLMs for robotic control.

LLMs trained in code completion can write Python programs from docstrings
[205]. Given natural language commands, these code-writing LLMs can be re-
purposed to write robot policy code. However, if the translation inaccurately
reflects the intended safety constraints, it could lead to either overly
restrictive behavior, preventing the robot from performing its tasks
effectively, or insufficiently stringent constraints, leading to safety
violations. Writing robot policy code entails using LLMs trained on code
completion to write the code from natural language commands [206]. However,
the robot policy code is less reliable for enforcing safety constraints than
verifiable safe operations that satisfy standards such as ISO 61508. The
approach assumes that all given instructions are feasible and lacks a
mechanism to predict the correctness of a response before execution. However,
due to their reliance on patterns in the training data, LLMs might generate
syntactically correct but semantically inappropriate code. They also struggle
with understanding physical constraints of the environment in which robots
operate. Additionally, generalizing plans across robotic embodiments is
brittle with current LLMs. This implies a limitation in what can be
generalized, particularly without extensive data collection and adaptability
of the generated policies.

##  7 Outlook

Biomedical research is undergoing a transformative era with advances in
computational intelligence. Presently, AI’s role is constrained to assistive
tools in low-stake and narrow tasks where scientists can review the results.
We outline agent-based AI to pave the way for systems capable of skeptical
learning and reasoning that consist of LLM-based systems and other ML tools,
experimental platforms, humans, or even combinations of them. The continual
nature of human-AI interaction creates a path to achieve this vision once
focused on preventing and learning from mistakes. Building trustworthy
sandboxes [207], where AI agents can fail and learn from their mistakes, is
one way to achieve this. This involves developing AI agents that perform tasks
and consider the boundary of their generalization ability, fostering natural
and artificial intelligence.

#### Declaration of interests

The authors declare no competing interests.

#### Acknowledgments

We gratefully acknowledge the support of NIH R01-HD108794, NSF CAREER 2339524,
US DoD FA8702-15-D-0001, awards from Harvard Data Science Initiative, Amazon
Faculty Research, Google Research Scholar Program, AstraZeneca Research, Roche
Alliance with Distinguished Scientists, Sanofi iDEA-iTECH Award, Pfizer
Research, Chan Zuckerberg Initiative, John and Virginia Kaneb Fellowship award
at Harvard Medical School, Aligning Science Across Parkinson’s (ASAP)
Initiative, Biswas Computational Biology Initiative in partnership with the
Milken Institute, and Kempner Institute for the Study of Natural and
Artificial Intelligence at Harvard University. A.F. is supported by the
Kempner Institute Graduate Fellowship. V.G. is supported by the Medical
Research Council, MR/W00710X/1. Y.E. is supported by grant T32 HG002295 from
the National Human Genome Research Institute and the NSDEG fellowship. The
authors would like to thank Owen Queen, Alejandro Velez-Arce, and Ruth Johnson
for their constructive comments on the draft manuscript. Any opinions,
findings, conclusions or recommendations expressed in this material are those
of the authors and do not necessarily reflect the views of the funders.

#### Author contributions

All authors contributed to the design and writing of the manuscript, helped
shape the research, provided critical feedback, and commented on the
manuscript and its revisions. M.Z. conceived the study and was in charge of
overall direction and planning.

Figure 1: Empowering biomedical research with AI agents. AI agents pave the
way for "AI scientists" capable of skeptical learning and reasoning. These
multi-agent systems consist of agents based on conversable large language
models (LLMs) and can coordinate machine learning (ML) tools, experimental
platforms, humans, or even combinations of them. Robotic agent, AI agent that
operates robotic hardware for physical experiments; Database agent, AI agent
that can information in databases via ‘function calling’ and APIs; Reasoning
agent, AI agent capable of direct reasoning and reasoning with feedback;
Hypothesis agent, AI agent that is creative and skeptical when developing
hypotheses, capable of characterizing its own uncertainty and using that as a
driver to refine its scientific knowledge bases; Brainstorming agent, AI agent
that generates a broad spectrum of research ideas; Search engine agent, AI
agent that uses search engines as tools to rapidly gather information;
Analysis agent, AI agent capable of analyzing experimental results to
summarize findings and synthesize concepts; Experimental planning agent, AI
agent that optimizes an experimental protocol for execution.

Figure 2:  Evolving use of data-driven models in research. Data-driven
approaches, from databases and search engines, machine learning, and
interactive learning models to advanced agent systems (Section 2), have
reshaped biomedical research throughout the last several decades.

Figure 3:  Diverse configurations of AI agents in biology – from an LLM-based
AI agent to a multi-agent system with AI models, tools, and integrated
physical devices. a. By programming an LLM with the role, one LLM-based agent,
equipped with memory and reasoning abilities, performs multi-modal perception
and utilizes a range of tools, e.g., web lab tools, to accomplish specified
tasks. b-e. Leveraging AI agents equipped with diverse roles, perception
modules, tools, and domain knowledge enables collaboration between agents and
scientists. This collaboration can adopt various schemes, such as expert
consultation, debate, brainstorming, and roundtable discussions. f. Multi-
agent systems can establish a self-driving laboratory wherein numerous agents
collaborate on multiple iterations of biological research assisted by humans.
Each cycle of research encompasses the generation of hypotheses, the design of
experiments, the execution of experiments both in silico and in vitro, and the
analysis of results. Computing agent, AI agent that utilizes computational
models as tools; Decision agent, AI agent that makes decisions in response to
given conditions; Database agent, AI agent that retrieves relevant information
from databases; Reasoning agent, AI agent capable of direct reasoning and
reasoning with feedback; Expert agent, AI agent that provides professional
consultation based on reliable sources, such as domain expertise, feedback
from human experts, and the results of specific tools. Hypothesis agent, AI
agent capable of skeptical learning and reasoning to generate hypotheses;
Planner agent, AI agent that devises plans for future actions; In silico/vitro
agent, AI agent that uses tools in silico or in vitro environment.

Figure 4: AI agents consist of four key modules: perception, interaction,
reasoning, and memory modules. Perception interprets multi-modal environmental
data. Interaction facilitates engagement with the environment, encompassing
human-agent interactions, multi-agent interactions, and tool use. Memory is
responsible for the storage and retrieval of knowledge, while Learning focuses
on the acquisition and updating of knowledge. Reasoning, with or without
environmental feedback, plays a crucial role in planning and decision-making
processes.

Figure 5: Components of biomedical AI agents. a. Use of a short-term memory
module to recall previous relevant experiments for small molecule inhibitor
design. b. Use of a long-term memory module to retrieve relevant information
for target selection for a disease. c. Use of reasoning without scientist
feedback in gene prioritization for phenotype analysis. d. Use of reasoning
with feedback from scientists to select an alternative experimental approach.

Figure 6: Challenges for AI agents in scientific discovery. a. Shown are
critical challenges–including robustness and reliability, evaluation
protocols, dataset generation, governance, and risks—alongside b. strategic
approaches to address them.

Autonomy levels | Scientific discovery |  Scientist-AI agent roles
---|---|---
Hypothesis | Experiment | Reasoning |
SkyBlue!5 Level 0: No AI agent |  None |  ML models perform predefined tasks, with no adaptive changes to the protocols |  None |  $\cdot$ Scientist defines the hypothesis and sometimes uses the output of ML models to help with their generation $\cdot$ Scientist defines the task to test hypothesis $\cdot$ Scientist completes tasks
SkyBlue!15 Level 1: AI agent as an assistant |  AI generates simple and narrow hypotheses that are direct composition of existing knowledge |  Narrow design of experimental protocols and utilization of in silico and experimental tools |  $\cdot$ Strong reasoning in a selected task $\cdot$ Multi-modal summary of findings $\cdot$ Use of experimental data and existing knowledge |  $\cdot$ Scientist defines the hypothesis $\cdot$ Scientist defines the series of tasks to test hypothesis $\cdot$ AI agent completes tasks
SkyBlue!25 Level 2: AI agent as a collaborator |  AI generates hypotheses that are an explicit continuation of data trends and known literature |  Design of rigorous experimental protocols and adept utilization of a broad range of ex silico tools |  $\cdot$ Identifying pioneering discoveries $\cdot$ Synthesis of concepts, not a summary of findings |  $\cdot$ Scientist proposes initial hypothesis and refines hypothesis with AI agent $\cdot$ AI agent defines the series of tasks to test hypothesis $\cdot$ AI agent completes tasks
SkyBlue!35 Level 3: AI agent as a scientist |  AI generates creative, de novo hypotheses that are indirect extrapolations from existing knowledge. |  Development of experimental methods unlocking new capabilities |  $\cdot$ Contextualizing pioneering discoveries $\cdot$ Concise, informative and clear conceptual links between findings |  $\cdot$ Scientist and AI agent together form hypothesis $\cdot$ AI agent defines the series of tasks to test hypothesis $\cdot$ AI agent completes tasks
Table 1: Levels of autonomy in AI agents. AI agents are characterized by four levels of autonomy in biological research, which are defined based on the capabilities of AI agents to complete different steps of the discovery process. At Level 0, there is no AI agent, and ML is used as a tool. Level 1 consists of AI agents as research assistants, where agents complete a set of narrow and specific tasks defined by scientists. At Level 2, AI agents act as collaborators and can use a broad set of tools to identify scientific discoveries. Still, they can only generate hypotheses that are a linear continuation of literature. Finally, at Level 3, AI agents act similarly to human scientists across several axes of human evaluation, capable of identifying and understanding pioneering discoveries and extrapolating novel hypotheses that cannot be derived from existing knowledge.  Table 2: Glossary of key machine learning terms. Term |  Description
---|---
Multi-modal foundation model |  Advanced algorithms trained on multimodal datasets that can process various data types, including text, images, biological sequences, and high-dimensional tabular readouts. This training allows them to perform a broad array of tasks through few-shot fine-tuning and prompting across domains with little to no additional training
Transformer architecture |  Deep learning model architecture that uses on self-attention mechanism to capture long-range dependencies in input sequence data
Large language model |  Machine learning model with parameters on the scale of billions, trained on vast amounts of text data to understand, generate, and interact with human language on a large scale
Generative pretraining |  Strategy for training a machine learning model in an autoregressive manner to predict the next token from given data tokens, facilitating a general understanding of data sequence likelihoods
LLM-based AI agent |  AI system capable of solving complex tasks within its environment by equipping large language model with modules for perception, interaction, memory, and reasoning
Embodied AI agent |  AI agent system that interacts with the physical world through a body. The embodiment enables the agent to learn and adapt from sensory feedback and physical interactions
Fine-tuning |  A training process of making small adjustments to a pre-trained machine learning model to improve its accuracy on a specific task or dataset
Instruction tuning |  A training strategy that fine-tunes a model using a dataset of instructions and corresponding outputs to enhance its ability to follow specific instructions
Reinforcement learning with human feedback |  A reinforcement learning strategy where an action model learns to perform tasks by receiving feedback from a reward model that mimics human preferences, guiding it to align with desired human behaviors
Prompting |  Techniques that provide specific text or other modal input instructions to guide the model in responding toward a desired output direction
Cross-modal alignment |  A training scheme to align the representation embeddings of models across various modalities
In-context learning |  Ability of models, such as LLMs, to achieve new tasks based on the context provided within contextual prompts, without requiring explicit model training

Table 3: Glossary of key biological terms. Term |  Description
---|---
Linkage disequilibrium |  A phenomenon in which two alleles occur so often in proximity in the chromosome that their association cannot be random
Single-nucleotide
polymorphisms |  Genetic variation consisting of the replacement of a single nucleotide in the DNA sequence
Genome-wide association study |  Approach that identifies genetic variations across the entire genome associated with a specific disease or complex trait
Pharmacogenetics |  Field of research that aims to understand individuals’ responses to different drugs based on their genetic factors
Experiment in-vitro |  Procedures and investigations that occur within a laboratory environment (e.g., in a test tube) and outside of living organisms
In silico modeling |  The use of computers to build simulations or experiments that recreate complex biological phenomena in order to be able to study and predict specific behaviors. For example, modeling of molecular dynamics
Mass spectrometry |  Analytical tools to characterize and identify individual molecules based on specific properties (e.g., mass-to-charge ratio)
Molecular docking |  Computational simulation tools used to predict how ligands bind to receptors
Retro-synthesis |  Techniques to design the synthesis of complex molecules by starting from the target and moving back to the original compounds
Crystallography |  Field of science studying the structure of atoms and molecules in crystals, which are solid materials whose compounds are ordered according to a very regular and ordered arrangement
Cryo-electron
microscopy |  Imaging techniques used to identify the 3D structure of bio-molecules with near-atomic resolution without the need for extensive sample preparation and with the overall preservation of the sample

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