In recent years, artificial intelligence has evolved beyond basic pattern matching to become capable of autonomous reasoning, multi-step planning, and even delegation. This transition — from passive tools to goal-driven, reasoning agents — marks the rise of agentic AI.

For the life sciences sector, and especially clinical development, this evolution arrives at a critical time. Clinical trials are increasingly complex, cross-functional, and data-intensive. Agentic AI offers not just faster tools, but the possibility of autonomous collaboration — teams of agents working in harmony to reduce burden, increase efficiency, and shorten timelines.

Here we explore the evolution of agentic AI and how higher levels of autonomy could transform clinical development from reactive execution to proactive, intelligent orchestration.

The evolution of agentic AI

Agentic AI evolves through distinct levels of capability. Each stage unlocks new functionality — from static models to ecosystems of communicating agents. Here’s a clear breakdown of the five major levels:

Each level builds toward intelligent autonomy. The transition from Level 3 to Levels 4 and 5 introduces intentional behavior, goal-setting, and inter-agent collaboration — the foundations of autonomous operations in clinical development.

Agentic AI in clinical development: A new operating model

Clinical development is not just complex — it’s interdependent. Every milestone relies on the seamless handoff and integration of data, code, documents, and decisions. Agentic AI, particularly at Levels 4 and 5, promises to re-architect this model.

Level 4: Planning and reasoning agents

These agents can independently break down goals, design execution paths, and adapt to changing environments. Here’s how they can drive value:

• Medical writing agents
  • What they do: Generate drafts for protocols, CSRs, and patient narratives.
  • How they help: Understand document structures, integrate real-time data, and adapt language for regulatory or clinical audiences.
  • Outcome: Faster document turnaround, reduced rework, and scalable writing support.

• Statistical programming agents
  • What they do: Develop and validate analysis code in SAS, R, or Python.
  • How they help: Plan logical sequences, debug outputs, and dynamically update based on protocol amendments.
  • Outcome: Accelerated code generation with built-in quality assurance.

• Information synthesis agents
  • What they do: Retrieve and synthesize information from multiple domains — scientific literature, regulatory guidelines, real-world data, health system policies, and reports on unmet medical needs.
  • How they help: Prioritize and contextualize inputs to support clinical design, indication selection, and risk-benefit assessments.
  • Outcome: Broader strategic alignment and better-informed cross-functional planning.

Level 5: Multi-agent systems

At this level, clinical development becomes an ecosystem of agents, each with a specialized role, working under the coordination of orchestrator agents that function like project managers.

• Orchestrator agents
  • What they do: Assign tasks, monitor progress, and realign workflows in real time.
  • How they help: Adjust deliverables dynamically as inputs change or downstream agents complete their tasks.
  • Outcome: Continuously managed, self-optimizing trial execution.

• Agent networks
  • Example: A data management agent processes raw datasets and hands outputs to a statistical agent, which triggers a writing agent to draft updated narratives — all autonomously.
  • Value: End-to-end automation with minimal human handoffs.
  • Outcome: Real-time trial updates and agility under pressure.

The benefits of the agent ecosystem

From automation to autonomy

Agentic AI reflects an evolution from “AI that assists” to “AI that takes initiative” — supporting actions, learning from experience, and extending expertise across domains. In clinical development, where complexity continues to rise and efficiency is critical, this shift offers a meaningful opportunity rather than just an advantage.

As we look toward Levels 4 and 5, we can imagine a future where trials increasingly manage themselves, where teams are supported by networks of intelligent agents, and where human professionals gain more space to focus on innovation, thoughtful oversight, and meaningful patient outcomes.

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