The promise of agentic AI in life sciences has never been greater. Pharmaceutical companies, regulatory affairs teams and clinical research organisations are racing to deploy autonomous agents capable of synthesising intelligence, generating insights and taking actions that once required entire teams of specialists. But behind the vendor pitches and proof-of-concept demos lies a messier, more instructive reality: building agentic AI that works in drug development is hard. It requires confronting the limits of large language models, rethinking data architecture and reimagining what productivity means in a regulated industry.
Insights from Clarivate, shaped through partnerships with customers at the intersection of pharmaceutical intelligence platforms and AI-enabled workflows, point to five lessons that consistently hold true:
1. Models make a difference — especially when reasoning is required
Not all large language models are created equal, and in life sciences applications, the gap is stark. Tasks like interpreting regulatory guidance documents, mapping a company’s drug portfolio against an incoming pharmacovigilance requirement, or synthesizing competitive intelligence from heterogeneous data sources require genuine multi-step reasoning. Models that have been trained for strong chain-of-thought reasoning consistently outperform their peers on these tasks — not marginally, but decisively.
In practice, this means that model selection is a strategic decision, not a commodity choice. Teams that default to the cheapest or most familiar model because the basic capabilities look similar are often disappointed when agents fail at the precise moments that matter: when the logic is complex, the stakes are high, and the margin for error is zero. Investing in frontier reasoning models for the most demanding agentic tasks — and reserving lighter-weight models for simpler extraction or formatting — is a pattern that consistently delivers better outcomes.
2. Use LLMs for what they are good at — not as encyclopedias
One of the most consequential architectural mistakes in early agentic deployments is treating the language model as a knowledge store. LLMs are extraordinary at inference: at recognizing patterns, constructing coherent arguments, and producing fluent, well-structured language. They are significantly less reliable as authoritative sources of pharmaceutical facts, regulatory precedents, or clinical trial outcomes. Their training data has a cutoff, introduces biases and contains noise. In a regulated industry, a hallucinated citation or an outdated drug status is not just an inconvenience — it can compromise a regulatory submission or a business decision.
The right architecture separates the knowledge layer from the reasoning layer. Ground your agents in authoritative, curated data sources — structured pharmaceutical databases, regulatory agency publications, validated clinical trial registries — and use the LLM to reason over that retrieved content and construct the output. This retrieval-augmented approach dramatically reduces hallucinations and makes the agent’s outputs auditable and defensible. The LLM is a brilliant analyst; make sure it is working from the right briefing documents.
3. Structured data is the quiet competitive advantage
Agentic AI systems that operate primarily over unstructured text — PDFs, regulatory documents, publications — are slow, expensive, and prone to extraction errors. Every time an agent must parse a document to answer a query, it burns tokens, introduces latency and risks misinterpretation. Organizations that have invested in properly structured pharmaceutical data — drug records with normalised fields, trial data mapped to controlled vocabularies, regulatory actions tagged by type and jurisdiction — experience dramatically faster time-to-insight and meaningfully lower inference costs.
The lesson is that data infrastructure is not a prerequisite for starting your AI journey — but it becomes the primary bottleneck as you scale. Teams that have treated structured data as a first-class asset find that their agents can pivot from a broad strategic question to a specific numerical answer in seconds. Those operating from document soup spend more time on extraction than on intelligence. As agentic workflows mature, the organizations with the cleanest, most structured data estates will hold a compounding advantage.
4. Fine-tuning is no longer the answer it once seemed
A few years ago, the conventional wisdom for deploying AI in a specialized domain like pharmaceuticals was to fine-tune a base model on proprietary data. This made sense when general-purpose models lacked the sophistication to handle domain-specific tasks without additional training. The landscape has changed. Modern frontier models arrive with a breadth and depth of scientific, regulatory and clinical knowledge baked in that would have seemed implausible three years ago. Attempting to fine-tune them for marginal gains often introduces instability and creates a costly maintenance burden — every time the base model is updated, the fine-tuning must be revisited.
The more effective investment is in prompt engineering, retrieval architecture and well-designed system instructions that shape the model’s behavior without altering its weights. Domain specificity is better achieved through what the model is given to work with — curated context, structured data, precise instructions — than through the expensive and fragile process of modifying the model itself. Fine-tuning retains legitimate use cases, particularly for stylistic consistency or highly repetitive structured extraction at scale, but as the default approach for domain adaptation it has largely been superseded.
5. Customers no longer want insights — they want actions
The expectations of pharmaceutical intelligence customers have undergone a quiet but profound shift. A year ago, delivering a well-structured regulatory impact summary or a competitive landscape report was considered high-value AI output. Today, customers increasingly want the agent to do something with that intelligence: draft the submission, flag the affected products in the system, schedule the review meeting, update the project tracker. The boundary between intelligence and workflow execution is dissolving.
This shift demands a rethinking of agentic architecture. Systems designed purely for information retrieval and synthesis need to be extended with action-taking capabilities — API integrations, document generation pipelines, task management connections — all wrapped in appropriate human-in-the-loop controls to manage the compliance and accuracy risks inherent in a regulated environment. Organizations that build for action from the outset will find themselves significantly ahead of those retrofitting action onto a passive intelligence tool. The agents that will define the next phase of AI in life sciences are not those that answer questions — they are those that close the loop.
The agentic AI era in life sciences is not arriving — it is already here, already in production, already generating real value and real lessons. The organizations learning fastest are those willing to be honest about where the first generation of deployments fell short and deliberate about building the next generation right. Model quality, grounded knowledge, structured data, lean architecture and action-ready design: these are not aspirational principles. They are what separate the agents who transform pharmaceutical workflows from those who generate impressive demos and little else.
Learn more about how Clarivate technology consulting helps customers optimize their technology capabilities here: Healthcare Technology Consulting Services | Clarivate
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