ReplicatorBench: Evaluation of AI Agents in Scientific Replicability

The evaluation of scientific replicability has become a central challenge for artificial intelligence applied to research. As AI agents take on complex tasks, such as the automatic analysis of articles or the execution of experiments, the need arises to measure their real capacity to reproduce or refute previous findings. This article explores how modern benchmarks are evolving to subject systems based on language models to exhaustive tests in three phases: retrieval of replication data, design and execution of computational experiments, and interpretation of results. Unlike previous approaches, focused only on reproducibility when the original code and data are available, the new methodologies incorporate both replicable and non-replicable cases, allowing the sensitivity of agents to inconsistent results to be evaluated. In this context, it is essential to have robust infrastructures that facilitate the integration of agents with search engines, sandbox environments, and data access APIs. Companies like Q2BSTUDIO offer AI solutions for businesses that allow building and deploying intelligent agents capable of orchestrating complex workflows, from information extraction to the generation of interpretive reports. Furthermore, the implementation of these systems requires custom software that adapts to the specific protocols of each scientific domain. The integration of aws and azure cloud services guarantees scalability to process large volumes of data, while business intelligence capabilities, with tools like power bi, allow visualizing agent performance metrics and detecting patterns in their errors. Recent findings indicate that current agents are skilled in the design and execution of experiments, but show significant weaknesses in retrieving new data sources, a critical point for genuine replicability. Addressing this gap involves not only improving the underlying models, but also designing modular architectures that integrate semantic search, open repository management, and data quality verification. Cybersecurity also plays a relevant role in protecting sensitive data during replication processes. In short, the rigorous evaluation of AI agents in scientific tasks opens the door to reliable automated assistance, provided it is combined with robust and customized technological platforms, such as those developed by Q2BSTUDIO, capable of transforming methodological challenges into operational solutions.