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The biopharma industry already operates like a multi-agent system, just without the agents.
Recent AI agent performance data is revealing: on focused tasks (two-hour sprints), top agents score four times higher than human experts. But stretch the same agent to a 32-hour task, and humans outperform it two-to-one. Agents achieve near-perfect success on tasks that take a human under four minutes; below 10% success on tasks that run over four hours.
The problem is structural. Making the agent smarter will not fix it, but redesigning the task will.
Source: Kwa et al. (2026). Measuring AI Ability to Complete Long Software Task. https://arxiv.org/pdf/2503.14499
Why the monolith fails, especially in pharma
The temptation to build one generalist agent and load it with context across regulatory, scientific, commercial, and clinical domains is inefficient. A generalist agent trying to manage regulatory compliance and synthesise scientific evidence and model reimbursement pathways will simultaneously hallucinate across domains won't understand deeply enough. It might be 90% right on average, but you can never know which 10% is wrong. In biopharma, that's an unacceptable risk.
When a generalist agent fails, everything fails: no isolation, no fallback. One flawed reasoning step cascades through the entire chain. Software engineering learned this lesson in the 2000s. One giant codebase, impossible to debug, impossible to scale. The answer was microservices: independent modules, independent deployment, independent validation. The same logic now applies to agentic AI in biopharma.
The orchestration answer: specialised agents with human decision gates
If intelligence comes from the model, correctness comes from the architecture. To achieve this, this is what the architecture should looks like:
- Small, specialised agents, each excellent at one narrow task. A regulatory agent that only checks compliance. A science agent that only reviews the evidence. A market access agent that only models reimbursement. Each can be validated independently, tested against known outcomes, retrained without touching the rest of the system. Need to add a new domain? Add a new agent.
- Validation gates between agents. If the regulatory agent flags a problem, the pipeline stops before downstream agents waste cycles on an invalid pathway. Guardrails must be structural, as we explain here.
- Humans at the right moments: not reviewing every output, but sitting at decision gates between phases. The architecture defines where human judgement matters most, rather than distributing it thinly across every step.
Now, most industries adopting agentic AI have to build governance frameworks from scratch: who validates what, where humans sign off, how to create audit trails. But here's the kicker: Biopharma already has this.
The decision-gate architecture — specialised agents operating within human checkpoints — maps directly onto how pharma already works. Regulatory affairs, medical affairs, clinical development, market access: each function already has defined review cycles, sign-off requirements, and clear accountability structures. That is the multi-agent orchestration model, without the agents.
The vast sums of money that pharma spends on regulatory compliance, usually framed as overhead, is actually a head start: the governance infrastructure that other industries are scrambling to build, pharma already operates.
What this looks like in practice
Take medical content creation. A single piece of promotional content currently moves through approximately seven sequential review cycles across medical, legal, regulatory, and localisation. Each reviewer waits for the previous one. Average cycle: six to eight weeks.
Under an orchestrated multi-agent model, a drafting agent produces the initial version. Medical accuracy agents and regulatory compliance agents run in parallel. Localisation agents adapt for local markets simultaneously. Humans approve at two defined gates: after the initial draft and after the final review. The cycle collapses from weeks to days, while the quality controls remain intact.
The same logic applies across the pipeline: pharmacovigilance monitoring, regulatory submission drafting, protocol design, site selection, KOL mapping. In each case, the opportunity is not to replace human judgement, but to remove the coordination overhead that delays it.
The gap will be measured in pipeline years
Enterprise application of agentic systems is growing at nearly 50% compound annually, and the pattern is becoming the dominant architecture of enterprise AI. For pharma, the gap between organisations that get the orchestration layer right and those still experimenting with generalist agents will very soon be measured in years of pipeline advantage.
Philipp Diesinger leads the pharma Practice Area at Rewire.
Agents reward organisations that ask harder questions first.
The organisations getting compounding value from agentic AI share one trait: they invested in getting the foundations right before scaling.
We've built agent systems across financial services, energy, telecom, and public sector, from early proofs of concept to production systems running at scale. We've seen what separates the deployments that deliver from the ones that don't.
