Artificial intelligence already supports many laboratory processes, but most tools still operate within narrow limits. Gary Stimson, Principal Architect, Head of AI Technologies at LabVantage Solutions, and Denise Bell, Vice President, Product Management and Marketing at LabVantage Solutions, position agentic AI as the next step beyond analytics, machine learning, and generative systems. Stimson explains that earlier AI models were good at finding trends and analyzing data, but they still functioned mainly as analytical engines rather than active participants in workflow execution. That distinction matters because laboratories do not just need insight from data; they need systems that can help move work forward.
Stimson also draws a clear contrast between generative AI and agentic systems. Generative tools can be useful, but their outputs can vary from one prompt to the next, which makes them harder to rely on in structured laboratory environments. By contrast, agentic AI pairs a language model with a set of goals, allowing it to work toward an input, an output, and a defined task. Rather than simply producing an answer, it can help carry out a sequence of actions within a controlled process.
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Starting with Practical Workflows
That shift becomes more meaningful when applied to routine laboratory work. Stimson describes a model in which agents can pass tasks among themselves, call the right tools, and then confirm that the work has been completed correctly. “You can chain those together to get any job done,” he adds. This makes the concept especially relevant for laboratory environments built around traceability and verification. The real value lies less in conversation and more in orchestration.
Even so, the first gains are likely to come from modest use cases rather than sweeping transformation. Bell points to short, structured activities such as logging samples or summarizing worksheets as the natural entry point for adoption. She notes that these are very task-oriented applications that offer labs a practical way to test AI without overhauling established workflows. That approach also fits the culture of the lab, where trust tends to build through steady performance rather than bold promises.
Trust Still Shapes Adoption
For all the promise around agentic AI, technical capability is only part of the equation. Laboratories still face uncertainty around hallucinations, regulatory expectations, and data governance, especially in controlled environments. “There are not currently many regulations around how labs can use AI, which makes some organizations cautious about moving too quickly,” notes Bell. Trust in the reliability of outputs, and confidence that systems will operate within appropriate guardrails, remain central to adoption.
Data handling is just as important. Bell emphasizes that deployment models must address privacy and security concerns, particularly for organizations that do not want sensitive information moving into public or loosely governed environments. Stimson adds that in LabVantage’s model, there’s no data stored in the cloud at all. That kind of assurance will shape how quickly laboratories embrace agentic AI in production settings.
From Assistance to Autonomy
Stimson’s longer view centers on systems that monitor workflows, surface problems early, and eventually coordinate more of the lab on their own. He points to the possibility of agents that check instrument status in the background and alert users before calibration failures or related issues interrupt operations. That kind of predictive oversight would shift AI from a reactive helper to a more proactive operational layer. He also sees more natural interfaces ahead, including voice agents that allow users to interact with systems while they work.
The broader ambition is even more striking. Stimson describes “the dream” as a dark lab—where a sample moves through analysis with robotics and intelligent agents working together across the workflow. The burden of labor-intensive and admin tasks would be minimal for scientists in such a scenario. That vision remains aspirational, but the direction is clear: agentic AI in the laboratory is moving beyond prompt-based support and toward workflow control, with the potential to reduce repetitive work and free scientists to focus on interpretation, method development, and decision-making.
