Interest in the next generation of AI is rising in the pharma industry. In this sector-specific guide, ArisGlobal’s Jason Bryant assesses agentic AI’s promise and some of the prerequisites for safe and successful adoption.

What is Agentic AI?
Agentic artificial intelligence (agentic AI) involves the autonomous coordination of goal-driven AI “agents”. It is the most significant leap in AI’s development since the launch of ChatGPT three years ago, because of the technology’s inherent potential to redefine the way organisations operate and the value they deliver.
This is due to agentic AI’s ability to apply its own reasoning – a step change from previous incarnations of AI which involved the automation of predefined processes according to given rules. With agentic AI, there is much greater autonomy in what AI does and how. Prompted with the desired outcome, individual specialist agents each invoke their own intelligence, experience and reasoning to fulfil their part in the most effective way possible.
All of this is co-ordinated by an orchestrator agent. As well as optimising the end-goal delivery, the orchestrator uses the collective insights to propose new ways to add value. In other words, this next phase of AI isn’t just about productivity; it is about informing and enabling new forms of value creation – where autonomous, multi-agent reasoning drives new insight and decisions.

Why is This Significant for Pharma?
The ability to reason, anticipate, generate insight and knowledge and make better decisions is ideal for pharma, as an industry that is data-rich, process-heavy and outcome-critical.
Agentic AI is not just about doing the same things more efficiently and more accurately. It can help to challenge current processes and determine what else might be possible; what other opportunities might be leveraged.
So where is agentic AI now in pharma?
Pre-agentic AI is already enabling new cost-efficiency in R&D functions such as regulatory affairs and drug safety/pharmacovigilance. To date, this has tended to be in discrete areas such as marketing authorisation application preparation, product change control/regulatory impact assessment management, adverse event case processing, and safety reporting.
Agentic AI’s vision is more ambitious, potentially enabling step changes in the role played and value contributed by Safety, Regulatory and adjacent teams.

Making Pharmacovigilance More Pre-emptive
The use of AI to streamline Medical Dictionary for Regulatory Activities (MedDRA) coding of adverse events offers considerable potential to transform the value of pharmacovigilance.
Already, AI has helped boost efficiency and accuracy around the classification of adverse event data, with the potential to invoke additional reference cross-checks, or expedite next actions. Combining autonomous MedDRA coding with proactive signal triage could help to eliminate manual bottlenecks. If designated agents detect an unusual combination of coded terms, for example, they could raise an automated “probable signal” alert; prepopulate a signal report draft (including proposed case lists, timeline and supporting evidence snippets); and recommend a triage priority for human safety reviewers.
The time to first credible signal would be shortened, and experts freed to focus on ambiguous/novel cases and investigation design. Meanwhile the system could route high-risk clusters to epidemiology/medical affairs automatically and suggest immediate risk-mitigation actions (e.g., targeted communications, batch holds, enhanced monitoring), aiding human decisions.