Authors
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Jon Pauls Ph.D., BCMAS – Cognizant Global Medical Affairs
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Bratati Ganguly PhD, MBA, BCMAS – CEO & Founder, Medical Affairs Solutions
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Lisa Shaw-O’Connor D.C., BCMAS – Cognizant Global Medical Affairs
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Lestter Cruz Serrano M.D., BCMAS – Head of Cognizant Global Medical Affairs
Key Takeaways
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AI agents have existed for decades, but were largely theoretical until recently.
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2025 marked the shift from concept to practical workplace application.
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Organizations can now deploy AI agents to drive efficiency and measurable impact.
What Is an AI Agent?
An AI agent is a software that understands objectives, determines how to achieve them, and takes action; often by interacting with multiple systems and tools, retrieving information, and coordinating with people or other agents. While the concept of intelligent agents has existed for decades, 2025 marked a turning point when they became truly practical in workplace environments.
This shift has been driven by two converging advances. First, modern large language models (LLM) provide agents with flexible reasoning, contextual understanding, and natural language communication. Second, emerging integration frameworks allow agents to operate directly within the systems where work occurs: Customer Relationship Management platforms (CRM), document repositories, analytics platforms, and case-management tools, just to name a few. Unlike earlier chatbots that followed rigid scripts, today’s agents understand context, perceive their environment, plan intelligently, and take action – trust through meaningful responsibilities and clearly traceable contributions across enterprise workflows.
AI agents function like tireless digital colleagues. They can read, write, summarize content, initiate requests, run analyses, monitor signals, and escalate issues when anomalies arise. Crucially, they are goal-oriented; they do not merely respond to prompts but pursue outcomes. For instance, an agent asked to prepare briefing materials for an advisory board meeting can autonomously gather real-world evidence, synthesize and summarize publications, draft presentations, coordinate logistics, and document actions for audit and compliance, checking in with humans based on confidence or at predefined decision points. This evolution – from prompt → response to goal → multi-step execution fundamentally distinguishes agents from traditional AI assistants.
Capabilities of AI Agents
In Medical Affairs, this translates into practical capabilities: assembling evidence packs from approved sources, pre-briefing field teams ahead of HCP engagements, performing compliance checks on call notes and materials, and synchronizing follow-ups in CRM environments. Operating within defined permissions and auditable workflows, agents reduce administrative burden while improving consistency, traceability, and cycle-time performance. They do not replace expertise; they ensure routine execution is reliable, compliant, and scalable, allowing professionals to focus on scientific judgment and strategic engagement.
Agents can interact collaboratively: Review, evaluate, criticize, improve each other’s output. A literature-triage agent can curate and prioritize publications for a medical-writing agent; a review agent can verify relevance, confidence, objectivity, and originality; a compliance agent can validate claims against approved sources; and a scheduling agent can coordinate meetings while embedding consent language for downstream agents. When roles and handoffs are explicit and logged, organizations gain visibility into workflow progress, decision rationales, and where human intervention is required. In this way, AI agents bring structure, transparency, and accountability to Medical Affairs operations.
Context Is the New Code
A defining principle for next-generation AI agents is: context is the new code. Large language models serve as the reasoning engine; context fuels meaningful decisions within a specific organizational environment. Context extends beyond raw data to include workflows, decision rights, definitions, constraints, governance expectations, and culture.
Without context, an agent is a capable generalist but may miss critical nuances. With context, agents behave like well-onboarded colleagues: applying correct templates, respecting role boundaries, referencing approved sources, and escalating issues according to established practices.
This has led to a new discipline called “context engineering”. Organizations curate reusable context assets: reference taxonomies, policy and compliance packs, process maps, integration stubs, glossaries, and evaluation sets maintained as living documentation and delivered to agents at run time.
The practical impact is two-fold: First, agents produce outputs that are more consistent, compliant, and fit for purpose, reducing errors and rework by design. Second, organizations gain scalability. New agents can be deployed by connecting them to the same shared context fabric, rather than rebuilding logic and controls from scratch. In regulated environments, such as Medical Affairs, this approach enables faster innovation without sacrificing rigor, transparency, or trust.
Beyond efficiency, context engineering in agentic AI offers a unique incentive and opportunity to reinforce and elevate quality management in Medical Affairs. By systematically curating and updating the context assets that guide agent behavior – such as policies, SOPs, and compliance guardrails; organizations ensure that every agent operates with the latest standards and requirements. For example, if a new regulatory guideline or internal policy is issued, updating the context fabric ensures that all agents immediately adapt their workflows, documentation, and decision logic accordingly. This real-time alignment prevents the risk of agents acting on outdated or noncompliant instructions, which could otherwise compromise auditability and regulatory standing. In effect, context engineering transforms the ongoing maintenance of the Quality Management System (QMS) from a manual, error-prone task into a scalable, automated process – freeing human experts to focus on continuous improvement rather than routine oversight. Recent industry guidance and case studies highlight that organizations investing in robust context engineering see measurable gains in both compliance and operational quality.
Networks of Specialized Agents
Many organizations are moving from monolithic agents to networks of specialized agents that collaborate. Responsibilities are partitioned: a literature agent handles triage; a medical-writing agent produces drafts; a compliance agent validates claims; and a program-manager agent maintains task flow, timelines, and visibility.
Advantages include auditability, modular evolution, and continuous improvement. Declarative orchestration: defining agent roles, handoffs, escalation paths, and guardrails through configuration; allows domain experts to shape workflows directly without coding. The result is agent behavior closely aligned with real-world processes, transparent, governable, and adaptable.
For Medical Affairs, AI agents are moving from experimentation to operational reality amid increasing scientific complexity, regulatory expectations, and resource constraints. Context-aware, specialized agents allow scaling of insight and execution without compromising rigor, compliance, or human judgment. Organizations that codify context, clarify workflows, and design for auditability can transform AI from a productivity tool into a durable capability, strengthening scientific exchange, evidence generation, and trust with regulators, HCPs, and patients.
Grounding the Vision in Medical Affairs Scenarios
- Integrated Evidence Planning (IEP):
AI agents help coordinate data synthesis, real-world evidence insights, and documentation. Leading organizations building foundational data platforms and AI systems are developing agentic for evidence planning, while pharma companies pilot workflows for literature and feasibility assessments. - MSL–HCP Engagement:
Agents prepare tailored briefings by synthesizing recent publications, consensus positions, safety signals, and prior HCP interactions. Post-engagement, they summarize insights and propose compliant follow-ups. Human review remains central, but administrative burden is reduced, and insight capture is more consistent. - In-Room Augmentation (“Whispering”):
Early implementations provide discrete, consented support during virtual or post-engagement interactions. Context-aware agents monitor discussion themes, surface evidence, and flag compliance considerations. Today, support is primarily pre- and post-engagement, structuring notes, linking citations, highlighting insights, and routing follow-ups. Real-time “whispering” is limited to pilots or simulations but represents a future avenue as trust and governance mature. - Medical Information and Content Operations:
Agents assist in assembling response packages from approved repositories, populate metadata, and initiate review workflows. Companion compliance agents check for prohibited claims, outdated references, and policy deviations. Governance layers capture audit trails, while human experts retain final judgment. - Pharmacovigilance-Adjacent Workflows:
Agents support literature surveillance, case-narrative standardization, and cross-functional signal sharing. Grounded in safety policies, they accelerate information delivery without compromising oversight, accountability, or regulatory standards.
Key Challenges
Proper Oversight and Compliance:
AI agents in regulated environments must be reliable, auditable, and aligned with policies. Graduated autonomy is essential: humans remain in the loop for high-stakes decisions, with explicit review points. Governance frameworks vary by risk, including policy grounding, guardrails, secure access, and consent procedures.
Transparency and Consent: Regulatory frameworks require clear disclosure when individuals interact with AI, with explicit labeling of AI-generated content. For in-room support, this includes obtaining HCP consent and retaining artifacts for audit.
Regulatory Posture:
Assistive AI follows lifecycle governance principles: performance monitoring, bias assessment, and controlled updates, inspired by FDA guidance (e.g., Predetermined Change Control Plans). Even where not classified as medical devices, similar disciplines are applied to mitigate risk.
Human Oversight:
Agents augment expertise rather than replace it. Fully autonomous agents remain limited due to governance, security, and trust considerations. Adoption focuses on decision support rather than delegation of authority.
Trust by Design:
Predictable behavior, consistent outputs, and traceable decisions are essential. Guardrails, evaluation sets, and ongoing monitoring stabilize agent behavior, moving AI from experimental to dependable operational use.
Phased and Responsible Adoption Strategy
- Leading organizations in pharma are adopting AI agents through a staged, risk-based approach:
Context Engineering: Curated policy packs, approved sources, role definitions, templates, and process maps are there to guide agent behavior. Additionally, version and test assets for consistency.
Transparency and Consent: Implementing standardized disclosures and opt-in mechanisms for HCP interactions are in place, thereby retaining consent artifacts for audit.
Shadow Deployment: Early agents support MSLs non-autonomously, allowing teams to refine guardrails and assess usefulness without risk.
Validated Scaling: Expansion occurs within validated systems after monitoring and quality thresholds are met.Clear human review points ensure safety, regulatory, and reputational risks are managed. Context engineering, observability, and responsible human oversight enable scaling without compromising judgment or compliance.
Human Factors: Trust, Change Management, and Psychological Safety
Successful adoption of agentic AI in Medical Affairs hinges on human factors as much as technology. MSLs and scientific professionals must trust that AI augments, not undermines, their expertise. Research shows acceptance depends on reliability, explainability, and shared accountability; without these, algorithm aversion can occur. Building trust requires prioritizing psychological safety, offering structured training, encouraging open dialogue on AI versus human judgment, and creating feedback loops for errors. True trust comes from transparent, supported collaboration between people and systems.
Bias and Data Provenance
AI agents are only as reliable as the data and assumptions behind them. Bias in data, literature, or training sets can distort insights, signals, or populations represented. For regulated tasks, it is essential to implement bias-detection, ensure inputs are context-representative, and monitor performance across subpopulations. Robust data provenance – tracing every piece of information to authoritative sources ensures transparency and prevents AI from reinforcing inequities or undermining scientific credibility.
Ongoing Cost and Maintenance of Context Engineering
For context engineering: curating and versioning policies, taxonomies, templates, and organizational knowledge is essential but resource-intensive. Enterprises with hundreds of data sources spend significant effort updating, testing, and reconciling context as regulations, content, and workflows evolve. Unlike “one-off” model training, context engineering is ongoing: version-controlled, aligned with policy, and subject to quality metrics. Proper planning, ownership, and tooling are critical to balance investment against efficiency and compliance benefits.
Looking Ahead
AI agents are not a replacement for human expertise: they are enablers of it. By treating context as a first-class asset, implementing robust oversight, and adopting a phased, consented approach, Medical Affairs teams can harness agentic capabilities to streamline routine work, enhance compliance, and surface insights more efficiently. The payoff is clear: field teams that are better prepared, evidence strategies that are more cohesive, and scientists now can focus on the judgment and relationships that drive meaningful impact. When designed thoughtfully, AI becomes a trusted teammate: one that augments, rather than replaces, the nuance and rigor at the heart of Medical Affairs.
