Pharma teams work in one of the most regulated environments in the world. Every task must be reviewed, approved, and saved as proof. That makes work slow, even when teams are doing their best.

This is why more companies are exploring AI agents in pharma. Not to replace people, but to help teams move faster without breaking compliance.

This approach is called agentic operations in life sciences.

Quick answer

Agentic operations in life sciences use AI agents to complete work across full workflows, while saving logs, following rules, and pausing for human approval when needed. This matches the expectations in the NIST AI Risk Management Framework, which stresses control, oversight, and proof for AI systems.

What agentic operations mean for pharma teams

An AI agent is not just a chatbot that answers questions.

An agent can:

• Understand a task
• Decide the steps needed
• Use tools to do the work
• Record what happened
• Ask a human to review before final approval

This idea of agents that can reason and take actions is described in Salesforce Agentforce, where agents plan tasks and call approved tools instead of just generating text.

A simple way to think about agentic operations is:

Trigger → Agent thinks → Agent acts → System logs → Human approves

Logging is critical. Pharma teams must keep records that show who did what and when. This is required under 21 CFR Part 11, which governs electronic records and audit trails.

👉 If you are unsure whether your workflows meet these requirements today, CI Life can help you assess where AI agents fit safely.

Agentic AI vs automation in pharma

Many teams already use automation, but automation has limits.

• Automation follows fixed rules and breaks when things change
• Copilots help humans, but humans still do most of the work
• AI agents can handle many steps on their own, within limits

This difference is clear in how cloud platforms work. For example, AWS Bedrock Agents allow agents to call tools and complete tasks, while Bedrock trace events record each step for review.

That is the real difference in agentic AI vs automation. Automation moves tasks. Agents manage work.

Why small changes cause big delays in Pharma operations

Pharma work is slow because it is built on proof.

A small change to content can restart the full MLR review process. This creates delays, rework, and frustration. Research from Veeva shows that improving review workflows can cut review time by 57% and reduce time spent in review meetings by 55%.

That tells us something important. The biggest problem is not effort. It is how the work flows.

Agentic operations help by:

• Catching issues earlier
• Reducing back-and-forth
• Saving approval evidence automatically

👉 If MLR delays are slowing your launches, CI Life can help map where AI agents reduce rework before review even starts.

Where AI agents deliver fast value

Not every workflow should start with AI agents. The best early wins are tasks that are high-volume and repeat often.

Five strong places to start:

1. Intake and routing

Agents sort requests and send them to the right team with clear logs.

1. Compliance checks before review

Agents flag missing claims or outdated references using approved libraries, similar to central claims libraries in MLR.

1. Content assembly

Agents build drafts only from approved content blocks.

1. Medical information drafts

Agents pull answers from approved sources and send drafts for review, following human-in-the-loop AI guidance as stated in the NIST AI RMF.

1. Audit evidence collection

Agents gather approvals, versions, and logs, which supports FDA Part 11 guidance.

Human review is required, not optional

In AI in regulated industries, people must stay accountable.

Good agentic systems include:

• Role-based access
• Clear approval steps
• Locked activity logs
• Source tracking

Data access also matters. HIPAA’s minimum necessary rule limits how much patient data any system, human or AI, can use. Agents must follow the same rule.

Enterprise platforms already support this idea through Microsoft Purview audit logs and Salesforce AI audit trails.

👉 CI Life designs agentic systems with these controls built in from day one. Talk with us if audit readiness is a concern.

When agentic operations make sense

Use agentic operations when:

• Tasks repeat often
• Rules and data can be approved
• Logs and proof are required
• Humans can review final decisions

Avoid them when:

• Work cannot be clearly defined
• Data access cannot be limited
• No one owns the process

Learn more about workflow design

Agentic operations work best when workflows are designed well. To understand why handoffs and systems matter so much, read:

👉 The Hidden Power of the Content Supply Chain

Final thought

Agentic operations are not about removing people. They are about removing friction, saving proof automatically, and helping teams work at the speed compliance allows.

👉 If you want to explore AI agents in pharma safely, speak with CI Life.