Executive Summary

Artificial intelligence in life sciences is moving beyond systems that simply generate answers or analyze information.

The next stage is agentic AI: AI systems capable of pursuing defined objectives, reasoning through multi-step tasks, using digital tools, and taking actions with varying levels of human oversight.

For pharmaceutical and biotechnology companies, this creates a significant opportunity.

An AI agent could potentially review scientific literature, analyze research data, coordinate workflows, monitor clinical trial activities, identify manufacturing risks, or support regulatory processes. Instead of requiring employees to initiate every individual task, agents can increasingly manage sequences of activities toward a defined outcome.

The technology is still developing, and highly regulated environments require strong controls. Agentic systems must operate within clear boundaries, maintain appropriate auditability, and keep humans accountable for consequential decisions.

Nevertheless, agentic AI could become an important evolution of enterprise AI.

The competitive opportunity will not simply be deploying AI agents. It will be redesigning workflows so that people and intelligent systems can work together more effectively.

What Is Agentic AI?

Agentic AI refers to AI systems designed to pursue goals through a sequence of actions rather than simply responding to individual prompts.

A conventional AI application might summarize a clinical document when asked. An agentic system could potentially identify relevant documents, analyze them, compare findings against predefined requirements, produce a report, and route the result to the appropriate employee.

Agents can potentially reason through tasks, access approved tools and data sources, remember relevant context, and adapt their actions based on results.

The level of autonomy can vary.

Some agents may only recommend actions. Others may execute approved tasks automatically. In life sciences, this distinction is particularly important because the consequences of incorrect actions can be significant.

Why Is Agentic AI Important for Life Sciences?

Life sciences organizations manage thousands of complex workflows.

Drug development requires coordination across scientists, clinical teams, regulatory specialists, manufacturing organizations, data experts, and external partners.

Many activities involve repetitive information gathering, analysis, documentation, and coordination.

Agentic AI could automate parts of these workflows rather than simply automating individual tasks.

This creates the possibility of moving from task automation toward workflow automation.

For example, instead of asking an employee to manually gather information from several systems, an agent could retrieve approved data, analyze it, identify exceptions, and prepare a result for human review.

How Could Agents Transform Drug Discovery?

Drug discovery generates enormous volumes of scientific information.

Researchers need to evaluate literature, genomic datasets, molecular structures, experimental results, and other evidence.

AI agents could help coordinate these activities.

An agent might identify relevant research, compare findings across sources, analyze experimental results, and suggest potential next steps for researchers.

Multiple specialized agents could eventually collaborate, with one analyzing literature, another examining molecular data, and another evaluating experimental results.

Scientists would remain responsible for interpreting evidence and making critical decisions, but agents could reduce the amount of manual information processing required.

Can Agentic AI Improve Clinical Development?

Clinical development involves complex workflows that span protocols, sites, patients, data, safety, and regulatory requirements.

Agentic AI could support these processes by continuously monitoring information and initiating predefined actions.

Potential applications include:

Protocol feasibility analysis
Site performance monitoring
Recruitment intelligence
Data review
Trial documentation
Safety signal triage
Study status reporting

For example, an agent could monitor recruitment data, identify sites falling behind expectations, investigate relevant operational information, and prepare recommendations for clinical teams.

This could enable earlier intervention rather than waiting for periodic reviews.

How Could Agents Change Clinical Operations?

Clinical operations contain many repetitive coordination activities.

Teams may need to track site communications, review documentation, monitor milestones, reconcile information, and prepare reports.

Agentic AI could connect these activities into more continuous workflows.

An agent could monitor predefined indicators, identify an exception, gather relevant context, and route the issue to the appropriate team.

This could reduce administrative workload while improving operational visibility.

However, agents should not be given unrestricted authority over clinical decisions. Actions involving patient safety, study integrity, or regulatory obligations require appropriate human oversight.

What Role Could Agentic AI Play in Medical Affairs?

Medical affairs could become an important area for agentic AI.

Medical teams manage scientific literature, field insights, medical inquiries, congress information, and interactions with healthcare professionals and key opinion leaders.

Agents could continuously monitor approved information sources, summarize new evidence, organize insights, and help teams identify emerging scientific themes.

They could also support medical information workflows by retrieving approved content and preparing draft responses for expert review.

This could allow medical professionals to spend more time on scientific engagement and strategic activities rather than information retrieval and administrative work.

How Could Agents Transform Manufacturing?

Manufacturing environments generate continuous streams of operational information.

Agentic AI could monitor equipment data, process parameters, quality indicators, and production schedules to identify potential issues.

When predefined conditions are met, an agent could initiate approved workflows, such as creating an alert, gathering relevant information, or notifying a responsible team.

Combined with digital twins and predictive analytics, agents could potentially evaluate different scenarios before recommending an operational response.

The long-term opportunity is a manufacturing environment where AI continuously monitors processes and helps coordinate responses.

Can Agentic AI Improve Regulatory Operations?

Regulatory teams manage large quantities of documents, submissions, requirements, and correspondence.

Agents could help track regulatory changes, organize submission materials, identify missing information, and compare documents against predefined requirements.

They could also support regulatory intelligence by monitoring approved sources and summarizing relevant developments.

However, regulatory interpretation remains a high-accountability activity.

AI agents can assist with information processing, but final regulatory judgments and submissions should remain subject to qualified human review.

Why Does Data Infrastructure Matter?

Agentic AI depends on access to reliable information.

An agent that cannot access relevant enterprise data will have limited ability to complete complex workflows.

Pharmaceutical companies therefore need strong data platforms, application programming interfaces, identity controls, and governance frameworks.

Data must also be sufficiently accurate and contextualized.

This means agentic AI may accelerate the importance of unified data platforms across life sciences organizations.

The companies with fragmented systems and poor data quality may struggle to scale agents effectively.

What Are the Risks of Agentic AI?

Greater autonomy creates greater risk.

An AI system that only generates text can be reviewed before use. An agent capable of taking actions can potentially create consequences before a person intervenes.

Key risks include:

Incorrect or unsupported decisions
Unauthorized system access
Data privacy issues
Model hallucinations
Inadequate audit trails
Automation bias
Unclear accountability

Life sciences organizations therefore need controls around permissions, monitoring, escalation, validation, and human oversight.

The principle should be simple: the greater the potential impact of an agent’s action, the stronger the controls should be.

How Should Pharma Govern AI Agents?

Governance must extend beyond traditional AI model management.

Companies need to define what each agent is allowed to access, which actions it can perform, when human approval is required, and how every action is recorded.

Agents should operate within clearly defined boundaries.

Organizations also need mechanisms for monitoring agent performance, detecting unexpected behavior, and disabling systems when necessary.

A risk-based approach is particularly important. Administrative workflows may support greater automation, while activities involving patient safety, product quality, or regulatory decisions require much tighter controls.

What Should Life Sciences Leaders Do Now?

Companies should begin with focused workflows where agentic AI can deliver measurable value without introducing unnecessary risk.

Document analysis, research intelligence, reporting, workflow coordination, and information retrieval are potential starting points.

Leaders should also identify processes that are fragmented, repetitive, and dependent on significant manual coordination.

The objective should not be deploying the maximum number of agents.

It should be redesigning selected workflows around a combination of human expertise and machine autonomy.

What Will the Future of Agentic AI Look Like?

The long-term vision is an enterprise in which employees work alongside networks of specialized AI agents.

A researcher could delegate literature analysis to one agent while another examines experimental data. Clinical teams could use agents to monitor trial operations. Manufacturing agents could continuously monitor production. Medical affairs teams could use agents to organize scientific intelligence.

These agents could potentially communicate with one another through governed enterprise systems.

Employees would increasingly become supervisors, decision-makers, and strategists rather than manually executing every step of complex workflows.

Conclusion

Agentic AI represents a significant evolution in how life sciences companies can use artificial intelligence.

The technology moves beyond generating information toward executing multi-step workflows, coordinating activities, and taking approved actions.

Its applications could span drug discovery, clinical development, medical affairs, manufacturing, regulatory operations, and commercial functions.

But autonomy must be matched by accountability.

Pharmaceutical companies will need strong data foundations, governance, validation, cybersecurity, and human oversight before agents can be deployed at scale.

The most successful organizations will not simply give AI more autonomy. They will carefully determine where autonomy creates value, where humans must remain in control, and how the two can work together.

Agentic AI could ultimately become the operational layer of the digital life sciences enterprise—turning AI from a tool employees use into an intelligent collaborator that helps organizations continuously execute, learn, and improve.

Life Sciences organizations are increasingly exploring agentic AI as a way to move beyond basic automation and deploy AI systems capable of planning, reasoning, using tools, and completing multi-step workflows. Recent research highlights applications across drug discovery, clinical development, and scientific research.

Life Sciences Move Toward Autonomous Workflows

Life Sciences companies can use agentic AI to connect data sources, specialized models, software tools, and laboratory systems. Instead of simply generating an answer, an AI agent can potentially retrieve information, analyze results, plan the next step, and coordinate tasks across a workflow.

Research published in Drug Discovery Today describes agentic systems that combine large language models with perception, computation, action, and memory capabilities for complex drug-discovery workflows.

Life Sciences Need Strong AI Governance

The expansion of agentic AI also introduces significant challenges. Life Sciences companies must address data quality, privacy, cybersecurity, model reliability, auditability, and regulatory compliance.

For regulated workflows, human-in-the-loop controls are particularly important. Recent research emphasizes grounding, human review, auditability, prospective validation, and governance before agentic systems are used for real-world decision-making.

Life Sciences and Autonomous Laboratories

The combination of agentic AI and laboratory robotics could create more automated research environments. AI agents can potentially plan experiments, interact with scientific instruments, analyze results, and use those results to refine subsequent experiments.

This emerging model could help Life Sciences companies shorten research cycles while allowing scientists to focus more heavily on scientific strategy and interpretation.

Life Sciences Prepare for the Next AI Era

Agentic AI represents an important evolution from conventional AI and generative AI. Rather than functioning only as a prediction or content-generation tool, agents can coordinate multiple activities across complex workflows.

For Life Sciences companies, the long-term opportunity lies in combining agentic AI with trusted data, specialized scientific models, automation, and strong governance. Organizations that establish these foundations could be better positioned to accelerate innovation while maintaining scientific and regulatory standards.

Life Sciences and Drug Discovery

Drug discovery is one of the most promising areas for Life Sciences adoption of agentic AI. Agents can support literature analysis, target identification, toxicity prediction, protocol generation, compound design, drug repurposing, and experimental planning.

Recent research suggests that agentic systems could connect computational analysis with laboratory automation, creating more continuous design-make-test-analyze cycles.

Life Sciences Transform Clinical Development

Life Sciences organizations can also apply agentic AI across clinical-development workflows. AI agents may assist with study planning, documentation, data review, patient-support activities, and regulatory authoring.

McKinsey’s analysis of more than 270 Life Sciences workflows found that 75% to 85% of pharmaceutical workflows contain tasks that could potentially be augmented or automated by agents.

However, these opportunities do not mean that AI should independently make high-stakes decisions. Human review, governance, validation, and clear accountability remain essential.

Life Sciences Benefit From AI-Powered Research

Agentic AI can help Life Sciences researchers manage large and fragmented datasets. Agents can connect scientific literature with experimental information and analytical tools, potentially reducing the time required to identify relevant evidence.

Early implementations reported in 2026 research have demonstrated improvements in speed, reproducibility, and scalability across selected drug-discovery workflows.

Editorial Team

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Agentic AI in Life Sciences