[Revised January 20, 2026]

Comparing AI Agents and AI Workflows in Pharmaceutical IT

Introduction

Artificial Intelligence (AI) is increasingly pervasive in the life sciences and pharmaceutical industry, from drug discovery labs to hospital networks. Two key paradigms have emerged for implementing AI solutions: AI agents and AI workflows. While both leverage AI to automate or augment tasks, they differ fundamentally in design and operation. As 2026 emerges as what industry experts call "the year of the agent" (^[1]), understanding the distinction between these approaches has become critical for pharmaceutical IT leaders. This report provides a technical comparison of AI agents versus AI workflows, with examples from pharma and healthcare, an analysis of their pros and cons in IT environments, and recent industry adoption trends. The goal is to help IT professionals in U.S. pharma understand where each approach fits best in their organizations.

Defining AI Agents vs AI Workflows

To ground the comparison, it is important to define what we mean by AI agents and AI workflows in an enterprise context:

What is an AI Workflow?

An AI workflow is a sequence of steps or processes that integrates AI capabilities into a predefined flow of tasks. In technical terms, an AI workflow augments traditional automation with AI models or algorithms at certain stages (^[2]). These workflows are typically structured and linear, meaning the sequence of actions is largely predetermined by developers or process designers (^[3]). For example, a workflow for handling customer emails might have explicit rules: “If an email mentions an order status, query the order database and respond with a status update; if it's a refund request, route it to accounting; otherwise ask for clarification.” Each step is predefined and triggered by specific conditions (^[4]).

AI workflows often use APIs or integrations to call AI services (such as machine learning models or large language models) as part of their steps (^[5]). This enables more adaptive decision-making than basic rule-based automation. For instance, a workflow might call a natural language processing API to understand an email's intent, or use a predictive model to prioritize a task. However, the workflow itself doesn't fundamentally change its structure on the fly – it follows the "rails" laid out by its designers (^[6]). In operational terms, AI workflows require organizations to map out business processes and insert AI where it adds value, but they remain controlled and predictable in execution.

Example (AI Workflow): In pharmaceutical R&D, an AI workflow might automate the pipeline of screening drug candidates. One step uses a machine learning model to filter promising compounds from a large database, the next step uses a predictive model (e.g. a QSAR model) to assess toxicity, and another step ranks the candidates for chemist review. Each stage is connected in a fixed sequence. A real-world illustration is the combined Recursion-Exscientia platform, which following their November 2024 merger, integrates phenomic screening with automated precision chemistry into a full end-to-end drug discovery platform. The system uses machine learning to analyze cellular images and screen thousands of compounds in parallel for potential drug candidates, leveraging over 60 petabytes of proprietary data (^[7]). This workflow accelerates identification of leads for rare disease treatments by following a scripted process (data generation → AI analysis → hits selection), achieving far greater scale than manual methods. Such AI-augmented pipelines exemplify how workflows can incorporate sophisticated AI while still operating within a defined procedural framework.

What is an AI Agent?

An AI agent is a more autonomous AI-driven system that can perceive its environment, make decisions, and act to achieve goals without needing step-by-step instructions for every action. In technical terms, an AI agent is given an objective and empowered to "figure out" the necessary steps or workflow by itself (^[8]). It can dynamically plan tasks, access various tools or data, and adjust its course of action based on context – essentially designing its own workflow to meet the goal (^[9]) (^[10]). AI agents might leverage advanced techniques like reinforcement learning, planning algorithms, or large language models with tool use to decide on their next actions.

Compared to a fixed workflow, an AI agent operates with much greater autonomy. After an initial prompt or goal, it can continue working without constant human prompts, drawing on its training and context to make decisions (^[11]) (^[12]). For example, instead of a static email-handling workflow, an AI agent might be told, "Help resolve this customer's issue." The agent will interpret the request, determine if it needs more information from the user, lookup data in databases, call APIs, or even collaborate with other systems, in whatever sequence it devises to solve the problem (^[8]). In essence, the agent is like an intelligent problem-solver that plans and adapts in real time, rather than executing a pre-written script.

From an operational standpoint, AI agents require robust frameworks to manage their autonomy. They often maintain persistent memory of past interactions and learn from experience, refining their behavior over time (^[13]). They may integrate multiple capabilities (NLP, computer vision, databases, etc.) within one entity, breaking down silos between systems (^[14]). This makes them powerful but also more complex to govern. AI agents in pharma could take the form of a virtual research assistant that autonomously assembles literature, generates hypotheses, and plans experiments. For instance, multi-agent systems have been trialed in healthcare settings: one example is using several AI agents in an emergency department to triage patients, where agents collaboratively adjust patient priority based on real-time vital signs and sensor data (^[15]). Another agent might simultaneously optimize hospital pharmacy inventory by predicting drug usage and reordering supplies just in time (^[15]). These agents operate in an environment with many uncertainties and make decisions on the fly, which a static workflow could not easily handle.

Key Differences at a Glance

In summary, the fundamental difference comes down to autonomy and flexibility. AI workflows are like trains on fixed tracks – efficient and reliable for predefined routes, but constrained to the path laid out (^[16]). AI agents are like self-driving cars – given a destination, they choose their own route, detour around obstacles, and even find shortcuts, offering more flexibility but with less predictability (^[16]). The table below highlights key comparative features:

Pros, Cons, and Ideal Use Cases

Both AI workflows and AI agents have roles to play in pharmaceutical IT, each with strengths and weaknesses. This section discusses the advantages and disadvantages of each approach, along with scenarios where they are most suitable in a pharma context.

AI Workflows in Pharma – Pros, Cons, Use Cases

Pros: AI workflows offer predictability, control, and ease of implementation in many cases. Because they operate in a constrained, defined manner, they are reliable for repetitive and well-understood processes (^[21]). This reliability is crucial in regulated environments like pharma, where compliance and traceability are important. Workflows are also typically easier to validate and govern – since each step is known, QA teams can verify that the AI components (such as a model making a decision) meet requirements before deployment. In terms of resources, implementing an AI-enhanced workflow often builds on existing automation or IT infrastructure (e.g. using existing data pipelines, RPA tools, or cloud AI services), which means lower development overhead than creating a fully autonomous agent. Many pharma companies have found quick wins by inserting AI into their workflows: for example, using natural language processing to automate pharmacovigilance workflows (scanning incoming drug safety reports for adverse events) or using machine learning in supply chain workflows to predict demand for drugs. These kinds of improvements can yield efficiency gains with relatively low risk.

Cons: The structured nature of workflows is also their limitation. AI workflows lack the flexibility to handle scenarios beyond what they were explicitly designed for (^[21]). If a process encounters an unexpected situation or a decision that wasn't encoded, the workflow might fail or require human intervention. In a cutting-edge pharmaceutical research environment, this can be a bottleneck – for instance, a rigid data processing pipeline might miss novel insights because it doesn't explore beyond its fixed logic. Additionally, while workflows integrate AI, they may not fully leverage AI's potential for learning and adaptation. Each workflow is often siloed to a specific task; it won't improve itself over time unless developers update the underlying models or rules. Operationally, maintenance can become an issue if too many specialized workflows proliferate – each one might need updates when data sources or APIs change. In summary, AI workflows trade away some innovation potential for stability. There's also the challenge of scaling workflows: they work great for defined tasks but orchestrating many workflows for complex end-to-end processes can become complicated (this is where agents or more holistic systems might be considered).

Ideal Use Cases: In pharmaceutical IT, AI workflows are ideal when the process is well-defined and critical to get right, but can benefit from AI to handle complexity within certain steps. Some prime use cases include:

• Data Processing and Analysis Pipelines: Many pharma companies deploy AI workflows to handle large-scale data analysis – for example, processing genomics or screening data. The workflow might sequentially: collect raw data, use AI to clean and normalize it, apply a predictive model to identify patterns, and then generate a report. This structured approach ensures reproducibility (important for research audits) while using AI for heavy lifting in the middle steps.
• Routine Administrative Tasks: Workflows augmented with AI are streamlining back-office operations in healthcare and pharma. A common example is insurance claims processing and medical record management – an AI workflow can extract information from documents (using OCR and NLP), classify cases, and route them appropriately, significantly speeding up what used to be manual paperwork (^[22]). These tasks have clear rules (often regulated), so a workflow can be designed to handle most cases, with AI adding some adaptability to different document formats or phrasings.
• Manufacturing and Maintenance: Pharmaceutical manufacturing benefits from AI workflows in quality control and maintenance. For instance, an AI-driven workflow can monitor sensor data from production equipment and predict when a machine is likely to fail or fall out of spec. If certain vibration or temperature readings deviate, the workflow's AI model flags a likely maintenance need, and the workflow automatically schedules a service or adjusts the process. This kind of predictive maintenance workflow was similarly used by Toyota to reduce equipment downtime by 50% (^[23]). In pharma, where equipment uptime and product quality are paramount, such workflows can save costs and prevent defects.
• Incremental Decision Support: Whenever a series of approvals or checks is needed (e.g. in clinical trial monitoring or regulatory submission preparation), an AI workflow can assist. For example, a workflow for clinical data review might automatically query disparate data sources, use AI to detect anomalies or safety signals in patient data, then compile a summary for a human reviewer. Each of these steps is predefined, but the AI adds insight (like highlighting a potential safety issue that a simple rule might miss). This ensures consistency and frees experts to focus on decisions that truly require human judgment.

By deploying AI workflows in such use cases, companies have seen measurable improvements. Camping World, though in a different industry, improved customer engagement by 40% and cut response times to 33 seconds by using AI-powered workflows to triage customer requests (^[24]) – an approach pharma firms are adapting for improving medical inquiries handling and other customer service areas.

AI Agents in Pharma – Pros, Cons, Use Cases

Pros: AI agents bring greater intelligence and autonomy to the table. Their biggest advantage is the ability to handle complex, open-ended problems that are difficult to fully hard-code in a workflow (^[21]). In the fast-moving world of pharmaceutical innovation, this can be a game changer. For example, an AI agent could act as a research assistant that not only searches databases but also formulates new research directions or adapts an experiment in real-time based on intermediate results. This level of autonomous decision-making can accelerate R&D by uncovering solutions a static program might not find. Agents are also excellent for situations requiring continuous learning and adaptation. In pharmacovigilance, one can envision an AI agent that continuously learns from new safety reports and literature – adjusting its detection algorithms on the fly and autonomously querying new data sources as they become available. Furthermore, AI agents can coordinate multiple roles. In a pharma supply chain scenario, one agent might monitor raw material inventory, another tracks production schedules, and another monitors demand; together, these agents could negotiate an optimal plan (this is the idea of multi-agent systems working in concert). Such distributed problem-solving is a strength of agent-based approaches. From a business perspective, when successful, AI agents can significantly reduce the need for human intervention in routine decision loops, potentially operating 24/7 and scaling on demand. This can lead to cost savings and faster operations – e.g., using an AI agent to handle after-hours support for drug information queries to doctors, where the agent can independently find answers from a knowledge base and only escalate the truly novel questions to a human pharmacist. A Deloitte report noted that AI's scalability (as seen in autonomous systems) provides unmatched advantages during peak demands, such as drug launches or regulatory submission crunch times (^[25]). Essentially, agents can pick up slack in ways workflows cannot when the environment gets very complex or dynamic.

Cons: The autonomy of AI agents comes with significant challenges and risks. Firstly, they are resource-intensive to build and maintain. Developing a capable AI agent often requires a dedicated team of data scientists, engineers, and domain experts. Major healthcare institutions like Mayo Clinic have assembled dedicated AI teams to work on AI and data-driven initiatives including advanced AI agents, illustrating the level of investment needed for cutting-edge AI deployments (^[26]). Even after deployment, agents need ongoing monitoring – because they can make unexpected choices, robust oversight (sometimes called human-in-the-loop) is necessary to ensure they don't go off track. IBM cautions that we are still in "early days" for fully autonomous agents and that human guidance is often needed to redirect or intervene when agents get stuck or produce undesired results (^[27]) (^[28]). One known issue is that if an AI agent's underlying model (say a large language model powering its reasoning) hallucinates or errs, the agent might come up with an invalid plan or loop endlessly (^[19]). These failure modes are less of a concern in rigid workflows. In terms of predictability, agents can be a double-edged sword – their lack of transparency means they might make a decision that's hard to explain, which is problematic in regulated areas (imagine an agent making a clinical decision without a clear rationale; this would raise compliance and ethical issues). Performance can also vary: an agent might solve a novel problem elegantly, but it might also take a convoluted route that wastes time or compute resources. As a result, validation and testing of AI agents is very difficult – you have to simulate many scenarios to trust an agent. Another con is cost: running a highly autonomous agent, especially one that uses complex models or searches large solution spaces, can be computationally expensive. In practice, many organizations find that simpler AI solutions give a better ROI for common tasks, and only invest in true agents for specific high-value applications. Finally, from a cultural perspective, deploying agents requires trust and change management – staff need to trust the agent to operate correctly, which can be a barrier in conservative, safety-focused industries like pharma. In summary, the power of agents comes with trade-offs in complexity, control, and risk that must be carefully managed.

Ideal Use Cases: AI agents shine in scenarios where the path to a solution is not straightforward or may change over time. In pharmaceutical and healthcare IT, such scenarios include:

• Adaptive Clinical Trial Management: Running clinical trials involves constant adjustments – enrolling the right patients, adjusting dosages, monitoring safety signals, etc. An AI agent could act as a trial coordinator that autonomously adjusts recruitment strategies or identifies trial sites that are underperforming and reallocates resources, all by analyzing real-time enrollment data and external factors. If one site faces delays, the agent could proactively open a new site or change patient allocation. A static workflow could not easily account for all the variables in a trial, but an agent can learn and adapt to maximize the chances of success. According to McKinsey's 2025 analysis, AI agents could deliver 35 to 45 percent productivity gains across clinical functions while cutting trial design timelines in half. One leading pharmaceutical company has already developed a multi-agent trial management co-pilot that monitors site activation, patient enrollment, and data management in real time.
• Drug Discovery "Auto-Scientist": In drug discovery, some companies are experimenting with autonomous science. For example, an AI agent could generate hypotheses for new drug-target interactions, plan and execute experiments in a robotic lab, and iterate based on results. This concept of a "self-driving lab" uses AI agents to make decisions about which compound to synthesize next or which biological assay to run in order to hit a research goal (^[29]). Agentic ecosystem platforms are now compressing the Design-Make-Test-Analyze cycle from weeks to days, turning the "Laboratory of the Future" into reality. Insilico Medicine's AI platform, for instance, uses a combination of generative models and reinforcement learning (an agent technique) to design new molecules and optimize them across multiple objectives (potency, toxicity, etc.) without explicit human instructions at each step (^[30]). In a landmark validation, Insilico's AI-designed drug Rentosertib (ISM001-055) for idiopathic pulmonary fibrosis achieved positive Phase IIa results published in Nature Medicine in June 2025, with patients receiving the 60 mg dose experiencing a mean lung function improvement of +98.4 mL compared to a -20.3 mL decline in placebo (^[31]). The entire preclinical development took just 18 months at a cost of approximately $2.6 million – orders of magnitude faster and cheaper than traditional drug discovery.
• Personalized Medicine & Patient Interaction: Consider an AI health assistant for patients on complex medication regimens. As an AI agent, it could autonomously monitor a patient's data (symptoms, wearable device readings, lab results), and adjust personalized recommendations or alert healthcare providers if it detects something worrying. It would need to decide when to just remind the patient to take a drug, when to suggest a doctor's visit, or even adjust a telemedicine appointment. Because each patient is unique, a one-size workflow might not suffice, but an agent could tailor its actions. Pharmaceutical companies are interested in such agents to improve medication adherence and outcomes. For example, agents could engage with patients in conversational dialogues, answer questions about side effects (leveraging a knowledge base), and escalate to live support only if necessary – acting as a round-the-clock care coach. This goes beyond a simple chatbot by enabling the agent to proactively reach out and change its interaction strategy per patient behavior.
• Complex Supply Chain and Manufacturing Optimization: Pharma supply chains are global and can be disrupted by many factors (e.g. raw material shortages, sudden demand spikes, geopolitical issues). An AI agent (or a team of agents) that oversees the supply chain can continuously adapt ordering and distribution. If a certain API (active pharmaceutical ingredient) shipment is delayed, the agent could reroute supplies from another warehouse or switch to an alternate supplier autonomously. Similarly, in manufacturing, an AI agent could dynamically adjust production schedules across multiple plants in response to real-time demand data or predictive analytics signaling a future shortage of a certain drug. These are complex decision problems where an agent's ability to handle "new, unpredictable problems" provides value beyond a fixed optimization model (^[21]). Indeed, IBM reports that AI agents have been used to predict drug shortages and adjust treatment plans accordingly in hospital settings (^[15]) – analogous principles can apply to pharma companies ensuring continuous drug supply.
• Autonomous Compliance Monitoring: Compliance in pharma (whether in manufacturing, marketing, or clinical trials) requires monitoring ever-changing regulations and operational data. An AI agent could serve as a compliance watchdog that reviews processes and documents, flags potential compliance issues, and even takes corrective actions. For example, if an agent scanning a pharmacovigilance database detects that certain adverse event reports exceed a threshold, it might autonomously draft a notification to regulators and initiate an internal review, following through the steps an audit team would take. This kind of agent needs to make judgment calls (is this signal significant or a false alarm?) and coordinate multiple actions, fitting the agent model well.

In these use cases, AI agents can handle complexity and uncertainty more gracefully than static workflows. However, it's worth noting that in practice many organizations will blend both approaches. A hybrid setup might use workflows as the backbone for standard operations, and deploy AI agents for specific functions that require extra intelligence. As one commentary put it, "most organizations will end up blending both: using an agent's flexibility for certain tasks but keeping an overarching workflow in place for others to avoid unnecessary chaos." (^[32]) This layered approach leverages the best of both worlds – the stability of workflows with the ingenuity of agents.

Market Trends and AI Adoption in Pharma

The pharmaceutical and healthcare sectors in the U.S. have seen a rapid uptick in AI adoption, both in workflow automation and in piloting agent-like systems. As of 2026, the industry has moved past the "inflated expectations" and "disillusionment" phases, entering what analysts describe as the "plateau of productivity" for organizations that established the necessary prerequisites (^[33]). Recent statistics and market research underscore this momentum:

Figure: Growth of AI in the pharmaceutical market. According to Mordor Intelligence, the AI in pharmaceutical market reached USD 4.35 billion in 2025 and is forecast to achieve USD 25.37 billion by 2030, advancing at a 42.68% CAGR. The AI in drug discovery market specifically is anticipated to grow from USD 24.51 billion in 2026 to USD 160.49 billion by 2035 (^[34]). North America dominates with approximately 46% market share, while Asia-Pacific remains the fastest growing region. This explosive growth reflects heavy investment and the high expectations for AI's value in the industry.

• High Adoption Rates: A majority of pharma organizations are already utilizing AI in some capacity. In a 2024 industry survey, 81% of pharmaceutical organizations reported using AI in at least one drug development program (^[35]). By 2025, 75% of pharmaceutical companies have made generative AI a strategic priority (^[36]). Capgemini research reveals that 23% of pharmaceutical and healthcare organizations have already adopted AI agents—leading all industry sectors—with 44% of healthcare executives reporting active agents in production environments (^[37]). On the enterprise side, pharma firms currently dedicate 8–15% of their R&D budgets to AI, projected to reach 20–25% by 2030. This represents a dramatic acceleration from just a few years ago when AI in pharma was limited to a few pioneers.

• Generative AI and Agents Hype Maturing: While 2023–2024 saw explosive interest in generative AI, 2025–2026 marks the transition from experimentation to production deployment. According to BCG analysis, the GenAI market in healthcare will grow at a compound annual rate of 85%—from $1 billion to $22 billion by 2027. McKinsey's analysis of 270 life sciences workflows found that 75 to 85 percent of pharma workflows contain tasks that could be enhanced or automated by agents, potentially freeing up 25 to 40 percent of organizational capacity (^[37]). Customer engagement in pharma is experiencing a noticeable shift as agentic AI moves from internal workflows into real customer-facing applications through platforms like Salesforce's Agentforce Life Sciences. However, caution remains warranted: unless projects are anchored in clear business value and governed carefully, over 40% of agentic AI initiatives are expected to be cancelled by 2027 according to industry analysts.

• Applications Driving Value: The pharma AI investment is driven by clear value propositions. McKinsey estimates that AI agents could lift growth by 5.0 to 13.0 percentage points in pharma and increase EBITDA by 3.4 to 5.4 percentage points over the next three to five years (^[38]). Key areas contributing to this value include accelerating drug discovery (AI can cut years off research timelines), improving clinical trial success rates, personalizing treatments, and automating manufacturing. The global pharmaceutical pipeline now features over 3,000 drug candidates developed or repurposed with AI assistance, with 530+ companies worldwide focused on AI-powered drug discovery as of late 2025 (^[39]). GenAI can accelerate early-stage drug breakthroughs by assisting in the discovery and optimization of drug candidates in silico, resulting in time reductions of 25% or more. Approximately 25% of biopharma companies reported that AI accounted for cost reductions and revenue increases of at least 5%, with industry leaders expecting that up to 30% of preclinical work could soon be accomplished using some form of AI (^[40]). Another data point: North America (led by the U.S.) accounts for approximately 46% of the global AI in pharmaceutical market revenue, reflecting the leadership of U.S. institutions in embracing AI. All these trends point to a strong appetite for both AI-powered workflows and exploratory agent solutions in the coming years.

• Challenges and Barriers: Despite high adoption, challenges remain. A common hurdle cited is the lack of AI expertise – 71% of organizations in pharma felt they don't have adequate expert staff for AI (^[41]). This partly explains the popularity of partnerships; nearly 79% of companies found AI partnerships valuable (collaborating with AI vendors or startups to fill skill gaps). Yet only 10.7% of companies have fully implemented AI across clinical activities – leaving significant first-mover upside (^[36]). Moreover, concerns about regulatory compliance and trust in AI outputs slow down deployment of autonomous systems. The regulatory landscape has evolved significantly: in January 2025, the FDA issued its first comprehensive draft guidance on AI for drug and biological product development, providing a risk-based credibility assessment framework. In January 2026, the FDA and EMA jointly published guidance outlining ten key principles for AI development in pharma, recommending models be "high quality, human-centric and compliant." Additionally, the FDA announced in 2025 its intention to phase out mandatory animal testing for monoclonal antibodies, creating fertile ground for AI-driven predictive safety and efficacy analyses.

In summary, the U.S. pharma and healthcare sectors are rapidly scaling up AI through a mix of intelligent workflows and production-grade agent deployments. The data shows a clear trend: almost all pharma companies are investing in AI, and usage is expanding from back-office automation to core R&D and clinical functions. The industry is witnessing a fundamental shift from pursuing efficiency gains to "doing better things," with companies focusing on innovation and transformational changes rather than mere cost reduction (^[42]). Over the next few years, the biggest gains are expected from end-to-end orchestration of research workflows, where multiple specialized agents plan, execute, and validate scientific tasks in coordination with human experts.

Conclusion

AI agents and AI workflows each offer distinct pathways to leverage artificial intelligence in pharmaceutical IT. AI workflows bring AI into structured processes, enhancing efficiency and decision-making in a controlled manner – they remain the foundation for many pharma applications, from research data pipelines to operational automation. AI agents, which have transitioned from experimental pilots to production deployments in 2025–2026, now tackle complex, adaptive challenges that static workflows cannot address. With McKinsey identifying that 75–85% of pharma workflows contain tasks suitable for agent enhancement, the potential for transformation is substantial.

For IT professionals in the pharmaceutical industry, the choice between deploying an AI agent or an AI workflow (or a combination) should hinge on the problem at hand. Nearly 40% of workflows are relatively standard and could be addressed by lower-complexity agents that business users can customize themselves, while another 50% are more complex, domain-specific workflows requiring custom-built agents (^[37]). If the task is well-bounded and reliability is paramount (e.g. automating a standard operating procedure or analyzing a known dataset), an AI workflow is the pragmatic choice. If the task is open-ended or highly complex (e.g. discovering a new drug mechanism or managing an unpredictable process), an AI agent or agent-based system may yield more innovative outcomes – with careful governance and human oversight.

One thing is clear: AI adoption in pharma is no longer a question of "if" but "how and how fast." With 81% of organizations already utilizing AI in drug development (^[43]) and investments climbing, those in IT leadership must architect solutions that are both cutting-edge and robust. The regulatory landscape is maturing, with FDA and EMA jointly providing clearer frameworks for AI integration. McKinsey estimates AI agents could lift pharma growth by 5–13 percentage points while increasing EBITDA by 3.4–5.4 percentage points over the next three to five years. By understanding the differences between AI workflows and AI agents, and by keeping abreast of industry trends and case studies, professionals can make informed decisions that harness AI effectively while managing risk. As 2026 marks the industry's transition to the "plateau of productivity," balancing these approaches will be key to driving the next wave of innovation in the pharmaceutical sector – whether it's streamlining operations or accelerating the delivery of life-saving therapies to patients.

Sources: This report drew on a range of authoritative sources, including industry case studies, academic reviews, and expert analyses. Key references include IBM's documentation on AI agents, McKinsey's 2025 analysis on agentic AI in life sciences, BCG's biopharma AI research, and the Virtasant industry report contrasting automation, AI workflows, and agents (^[26]). Market data draws from Mordor Intelligence and Towards Healthcare. Regulatory developments reference the FDA's AI guidance and the joint FDA-EMA AI principles. Additional industry surveys include the Norstella 2024 survey and Scilife report. These sources and others cited throughout the text provide further reading and evidence for the points discussed.