Industries · 10

AI agents for pharma and life sciences

In pharma and life sciences, traceability is part of the product: every adverse event report has deadlines, every batch needs reviewed documents, every task needs documented training. Computerised systems must be validated. Agents fit here when they are built exactly that way: with an audit trail, versioned prompts and documented tests.

Example agents

Three agents that take over work here

Examples from typical workflows, not client references. Which agents pay off for you is shown by assessing your own processes.

01

The pharmacovigilance intake agent

Drug safety · case intake

Reads incoming reports from email, call centre notes and literature, identifies potential adverse event cases, checks the minimum criteria, enters the data as a draft in the safety database and starts the clock.

Autonomy level

Start L3 · Assisted agents

Draft entry at L3; drug safety owns assessment and reporting.

Systems
Safety databaseEmail · call centreLiterature databasesDMS
Guardrails

When in doubt, treat as a potential case; no causality assessment, no submission to authorities, every entry with its source.

Human in the loop

Drug safety specialists assess every case and decide on reporting.

What to measure

Time to entry · missed cases in sampling · deadline adherence

02

The batch documentation agent

Quality assurance · release

Checks certificates of analysis and batch records against specifications, spots missing signatures, deviations and out-of-spec values and gives quality assurance a checklist with references.

Autonomy level

Start L3 · Assisted agents

Pre-review at L3; batch release stays with the responsible person.

Systems
LIMSQMSERPDMS
Guardrails

Read-only access, no release and no status change, every finding with its document reference.

Human in the loop

Quality assurance reviews the findings and releases or blocks the batch.

What to measure

Review time per batch · findings after release · release lead time

03

The training records agent

Quality management · people

Matches roles, SOP versions and training status, assigns due training, reminds employees and managers and produces the evidence for audits and inspections.

Autonomy level

Start L3 · Assisted agentsTarget L4 · Governed autonomy

Assignment and reminders at L4; exceptions such as task restrictions for missing training at L3.

Systems
LMSQMS · SOP managementHR systemMicrosoft 365
Guardrails

No performance assessment of employees, training data only, works council involved.

Human in the loop

Quality management decides on task restrictions; managers resolve backlogs.

What to measure

Training compliance per role · overdue trainings · audit findings

Framework

What to factor in for this industry

  • GxP and validation: Computerised systems in GxP environments are validated, for example following GAMP 5. For agents that means versioned configuration, documented evals and an audit trail that evidences every step.
  • Pharmacovigilance: Reporting duties and deadlines lie with the marketing authorisation holder's responsible persons. The agent helps not to miss a case or a deadline; it does not report on its own.
  • Data: Health data in adverse event reports is a special category under Art. 9 GDPR; training data is employee data and needs co-determination.

Where to start

The first agent: The training records agent

Training records follow rules, involve no patient data and quickly pay off in audits. A good place to practise validating and operating an agent.

Operated by BOTFORCE We do not just build the agents, we run them: monitoring, queues, evals and value reviews from one team.