Agentic AI in Life Sciences: Why Data Governance Comes First

The discussion around AI in the enterprise has evolved from chatbots that simply answer questions to agentic AI, where the AI does actual work by executing multi-step workflows. The question for the enterprise evaluating agentic AI is: What data foundations must be in place so that the AI agent can draft a payer email, summarize a site feasibility response, or respond to a medical question?

In the life sciences, where regulatory, compliance, and decision-driven business processes require a strong framework, the answer is obvious. Agentic AI, without data governance, does not produce outputs that can be trusted, audited, or automated. The data governance discipline, already critical for regulatory compliance, is now a prerequisite for meaningful deployment of agentic AI.

What Makes a Chatbot Different from an Agent?

What does agentic AI do, and why does that require data governance to be in place?

A chatbot simply produces text in response to a prompt. It consumes data but does not act on it. It does not execute a workflow. It does not produce machine-readable output. It does not create follow-up tasks. It simply answers a question based on the data consumed. An agent, on the other hand, does all of the above. It consumes data, executes a workflow, produces machine-readable output, and determines whether to create follow-up tasks. The agent does not simply read data and respond. It acts on the data, executes a multi-step process, and produces output that determines the automation, status, and task flow. The data the agent consumes determines the quality and accuracy of its output.

Three Life Sciences Workflows Where Data Governance Influences Agent Reliability

Life sciences has several workflows that involve large transaction volumes, repetitive processes, and reliance on unstructured data, all of which make these processes good fits for agentic AI. The following three processes highlight the role data governance plays in the design and development process.

Patient Services: Benefits Verification

Benefits verification and reverification processes are critical components of the patient services process. A specialist composes an email to the payer to request verification of coverage for a patient, plan, and drug combination. Once the payer has responded, the specialist interprets the response to obtain coverage, prior authorization requirements, copays, and restrictions. The specialist then enters the data into the case and schedules follow-up tasks for missing data.

In this case, the AI agent can perform both composition and interpretation. The agent will compose an email to the payer using the patient, plan, and drug information retrieved from the customer relationship management (CRM) database. The agent will interpret the payer’s response, enter the data into the case, and set follow-up actions to obtain missing data. The data governance consequence is that the quality of patient and plan data should be higher when processed by the AI agent than when processed by the specialist. This is because the specialist can obtain data and enter it into the case even when data are missing. This can be done because the data can be obtained from a different system. However, this will not be possible with the agent unless specifically coded to do so.

Clinical Operations: Site Feasibility and Selection

Before the clinical trial’s first patient is enrolled, the operations team assesses dozens of potential trial sites by sending feasibility questionnaires, collecting responses from the investigators, collecting IRB documentation, and developing readiness summaries. The agent may summarize unstructured responses into consistent, structured scorecards, provided the agent has access to governed reference data on investigators, facilities, and regulatory requirements.

The data governance challenge in the clinical trial example is the handling of unstructured data. The agent may have access to unstructured data in the form of free-text emails, PDF attachments, and filled-out forms. To develop a reliable summary, the agent needs access to a governed knowledge layer that includes structured data from the CRM and unstructured data from documents. Without the harmonization layer, the agent’s ability to develop a summary will be based on incomplete and/or incorrect data.

Medical Information: Healthcare Professional Inquiry Response

When a healthcare professional (HCP) initiates a clinical inquiry, the medical information team must respond based on content from approved sources, product monographs, prescribing information, and standard response documents. The agent’s response may be based on retrieval-augmented generation (RAG), in which the agent retrieves approved content before generating the response. This is the typical approach for the medical information team’s response to a clinical inquiry by a healthcare professional.

The data governance challenge for the medical information team’s agent is to determine whether the agent may retrieve approved content prior to generating the response. The agent requires access to a repository of approved content, subject to version control, access control, and regular curation. If the agent has access to unapproved content, such as drafts, outdated content, and competitor content, the agent’s response may be based on content that should not be provided to the HCP, a critical compliance error that would be avoided by manual processes.

The Data Governance Patterns That Enable Agentic AI

In each of the three use cases, similar data governance patterns emerge that organizations must address before deploying their AI agents in regulated environments.

Governed Retrieval: The agents will be able to retrieve information from CRM applications, knowledge bases, and other sources. Data governance dictates that the process by which agents retrieve information must be subject to the same data access controls, data quality, and data lineage as a human. “In regulated environments, data access policies must be enforced programmatically, not procedurally. An agent accessing data without permission, or accessing data in a way a human cannot, is a compliance risk.” (AI Governance Framework, 2025)

Structured Outputs: Agents produce outputs that trigger other automation actions, such as updating a case status, creating a task, or sending an exception notification. Agents must produce structured outputs, not unstructured text or prose. Structured outputs are a data governance requirement because they ensure the agent’s decision is auditable, testable, and reproducible. Organizations using agents whose outputs must be interpreted by humans fail to achieve the efficiency benefits of using an Agent in the first place.

Audit and Lineage: Regulated environments require traceability and auditability. Agents summarize a payer’s response or a feasibility questionnaire. Data governance requires the organization to store the original input data, the agent’s output data, the user ID of the person initiating the action, and the timestamp of the action. This audit pattern extends traditional data lineage concepts to AI agent outputs.

Human-in-the-Loop as a Governance Control: In the Life Sciences industry, human-in-the-loop is not a measure of the maturity of the underlying AI technology; it is a governance control. For example, the agent sends the draft of the payer outreach email to a specialist for review before sending it. The agent sends the draft feasibility response summary to clinical operations for review before it is considered.

Data Masking and Retention Policies: Data governance policies must be created to ensure that the appropriate information is masked prior to interaction with the AI model, that prompts or responses to the model are retained, and that the AI provider’s retention policies apply to the interaction with the Life sciences organization. Currently, the major enterprise-grade AI platforms can configure data masking and zero retention policies within their systems, but data governance policies for interactions with the system remain the responsibility of the life sciences organization.

Implications for Data Management Professionals

With the advent of agentic AI systems, data management teams face new responsibilities. Data quality that was “good enough” for human-mediated processes may no longer be sufficient when the same data is consumed by an AI agent without the mitigating effect of human judgment. Data management processes, such as master data management, reference data management, and content management, must be measured against a new standard: does the agentic AI system produce correct results from the data?

Organizations with a data governance foundation of clean master data, knowledge repositories, access control processes, and data lineage tracking can implement agentic AI systems with confidence. Organizations without such a foundation will find their existing data quality issues exacerbated using agentic AI systems.

The data governance discipline is not a necessary evil to be eliminated once the agentic AI system is deployed. The instructions to the agentic AI system, the data repositories from which the system retrieves data, and the system’s output schema must be version-controlled, reviewed, and maintained with the same discipline as any other data governance discipline. In regulated industries, data governance is the very foundation for the use of agentic AI systems, creating value or risk.