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Agentic architecture is the foundation of the current evolution of AI. It’s an AI system development approach that emphasizes autonomy, self-direction and self-improvement. This architecture supports multi-agent collaboration, integration with key enterprise systems and self-learning ecosystems. Instead of being programmed for specific tasks, AI agents in an agentic architecture continually evolve, shifting from task-based automation to proactive, AI-driven decision making.

Why Business And Technology Leaders Should Care

The shift to agentic AI represents a strategic transition from viewing AI as a tool to recognizing it as a strategic partner. This fundamentally alters how companies function and will redefine the roles of business and technology leaders and their interactions with AI moving forward.

A new tool has been developed to better assess the performance of AI models. It was developed by bioinformaticians at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and the Helmholtz Institute for Pharmaceutical Research Saarland (HIPS).

“DataSAIL” automatically sorts training and test data so that they differ as much as possible from each other, allowing for the evaluation of whether AI models work reliably with different data. The researchers have now presented their approach in the journal Nature Communications.

Machine learning models are trained with huge amounts of data and must be tested before practical use. For this, the data must first be divided into a larger training set and a smaller test set—the former is used for the model to learn, and the latter is used to check its reliability.

Kirigami is a traditional Japanese art form that entails cutting and folding paper to produce complex three-dimensional (3D) structures or objects. Over the past decades, this creative practice has also been applied in the context of physics, engineering, and materials science research to create new materials, devices and even robotic systems.

Researchers at Sichuan University and McGill University recently devised a new approach for the inverse engineering of kirigami, which does not rely on advanced computational tools and numerical algorithms. This new method, outlined in a paper published in Physical Review Letters, could simplify the design of intricate kirigami for a wide range of real-world applications.

“This work is a natural extension of our previous work on kirigami,” Damiano Pasini, senior corresponding author of the paper, told Phys.org.