Abstract
Multi-agent, multimodal artificial intelligence (AI) frameworks with scientific platforms represent more than an incremental advance—they mark a paradigm shift in computational research methodology, introducing unprecedented capabilities in real-time knowledge synthesis and autonomous scientific discovery. Drawing parallels with biological evolution, where simple components are optimized and recombined to create sophisticated functional structures, these AI frameworks employ analogous principles to develop autonomous problem-solving systems by predicting new connections and expanding established relationships. The emergence of higher-order capabilities from relatively simple AI building blocks enables these systems to tackle challenging problems in protein modeling, molecular mechanics, and engineering design. Physics-aware agentic models go beyond conventional data-driven inference, through their ability to self-improve, error-correct, and learn from real-time feedback, autonomously planning experiments, generating and executing code, and reasoning over results-effectively closing the loop in scientific discovery. Their inherent capacity to seamlessly integrate data-driven and physics-driven modeling approaches enables AI systems to independently formulate hypotheses, design simulations, and analyze outcomes, representing a significant advance in automated scientific investigation. Unlike prior surveys, this perspective emphasizes the emergence of higher-order cognition and reflection in multi-agent systems, framing them as autonomous collaborators rather than passive assistants. This perspective examines how this new paradigm is accelerating multidisciplinary breakthroughs in protein design, mechanics, and materials science, and explores its implications for the future of computational, theoretical and experimental research methodologies. We further highlight how agentic AI frameworks extend beyond science into domains of art and music, revealing deep isomorphisms across molecules, materials, and creative expression.