Adaptive Multimodal Agents-Based Framework for Automatic Workflow Execution

A research team has proposed a multimodal multi-agent framework designed to execute complex workflows automatically by learning from past execution data. The system, submitted for review on May 27, 2026, uses a two-phase pipeline to improve how autonomous agents navigate graphical user interfaces [1][2]. The framework addresses a limitation in current autonomous systems, which often treat tasks as isolated, linear steps. This approach prevents agents from understanding the underlying structure connecting different actions, reducing their effectiveness in new or changing environments [2]. The proposed architecture first runs an offline discovery phase that builds a topological knowledge base from fragmented execution logs. During live operation, agents use Adaptive Retrieval-Augmented Generation (RAG) to query this fixed graph, combined with a closed-loop collaborative verification protocol that allows them to self-correct and navigate dynamically [2]. The researchers report that this graph-based method leads to better task decomposition and adaptive navigation, and they validated the framework in a real-world context, noting it maintained high reliability and semantic awareness even with limited training data [2]. The work lands as the broader field of autonomous GUI agents continues to mature, with libraries such as TensorFlow providing foundational infrastructure for training and deploying the neural networks that often power such systems [3]. The framework's focus on multimodal perception and multi-agent collaboration also intersects with a wider ecosystem of robotics and automation software, which includes tools for computer vision, motion planning, and industrial robot programming [4]. The paper was submitted to the Computer Science section of arXiv under the Artificial Intelligence category [1][2].