What’s Hot in 2026: Agentic Laboratory Technology

In a time-critical field like drug development where data is everything, intelligent agents are poised to claim a seat at the bench. What is innovative today, will be inevitable in the laboratory of the future.

In the context of laboratory technology the word “agentic” refers to the capability of taking autonomous, goal-directed actions rather than simply executing fixed, passive instructions.  According to a 2025 McKinsey report agentic AI technologies could reshape up to 75-85% of workflows in life sciences R&D, offering potential productivity gains of 30-45%. Their Cognitive Generative Agent Readiness (CGAR) framework assesses readiness for agentic adoption across industries, pointing to pharmaceuticals as a “high-priority sector” due to the complexity and repeatability of laboratory-based tasks. These future systems leverage artificial intelligence (AI), robotics, automation, and data orchestration to operate autonomously or semi-autonomously within laboratory environments. 

The goal is to make drug discovery and preclinical research faster, smarter, and more reproducible.

At its core, agentic laboratory technology refers to software or physical agents that perform experimental or analytical tasks with minimal human intervention, adapting to real-time data and learning from outcomes. These agents can independently schedule experiments, operate instruments, analyze results, and make decisions to optimize workflows often across entire lab ecosystems. Examples include:

• ORGANA, a robotic lab agent capable of conducting complex chemical experiments while making decisions in real time.
• Artificial, a platform for orchestration of instruments and workflows across connected laboratories.
• Causaly’s Agentic Research Assistant, an AI agent that synthesizes literature, identifies drug targets, and supports hypothesis generation. 

This matters in preclinical research, because it often involves lengthy cycles of synthesis, screening, and analysis. Turnaround times can be reduced by automating repetitive tasks and intelligently optimizing experimental designs, thus significantly speeding up early-stage drug development. Agentic platforms enforce standardized protocols, real-time QC checks, and comprehensive metadata capture. This enhances data integrity and minimizes batch-to-batch variability, which is a known bottleneck in pharmaceutical R&D. And unlike traditional automation, agentic systems can learn from outcomes. For instance, if a set of compounds underperforms in vitro, the system can adapt the next batch of candidates using machine learning models. Adoption of agentic technologies is still early-stage, but momentum is growing. Startups and major pharma companies alike are investing in AI-native lab infrastructure to improve discovery cycles, reduce costs, and gain competitive advantage.

Christoph Eberle, PhD, Principal Scientist III, Charles River