.jpg)
Laboratory automation is moving towards an increasingly autonomous model, where artificial intelligence agents suggest the next experiments to be performed. However, efficiently planning and executing these tasks, considering real hardware limitations—such as multiple instruments with different capabilities and performances—remains a significant technical challenge. To address this, a two-stage approach has been proposed: first, constraint programming allows calculating optimal schedules that minimize total execution time while respecting each equipment's capabilities; second, a state dependency system between tasks ensures robust execution even in the face of unforeseen events. This model is not only applicable in materials chemistry but also lays the foundation for any orchestrator of critical processes in automated environments.
In practice, transferring this logic to business environments requires tailored software solutions that integrate optimization algorithms with existing infrastructure. At Q2BSTUDIO, we develop process automation platforms that combine artificial intelligence and AI agents to coordinate complex workflows. Our experience ranges from implementing AWS and Azure cloud services to creating custom applications that manage resource allocation in real time. Additionally, we offer business intelligence services with Power BI to visualize the performance of these systems, and cybersecurity audits that protect data integrity. All of this is aimed at enabling organizations in any sector to benefit from efficient and adaptable planning, similar to what is already achieved in the most advanced autonomous laboratories.
