Simon See

The integration of agentic AI into science discovery and engineering is poised to redefine workflows, accelerating innovation through autonomous reasoning, adaptive experimentation, and interdisciplinary synthesis. Unlike traditional AI models that passively assist with data processing, agentic AI actively formulates hypotheses, designs experiments, and refines methodologies in real time. This paradigm shift enables dynamic collaboration between AI systems and human experts, fostering breakthroughs in areas such as materials science, drug discovery, and sustainable engineering. Future workflows will leverage self-directed AI agents capable of navigating vast datasets, identifying novel patterns, and optimizing complex simulations—significantly reducing the time between theoretical conception and practical application. By integrating principles from neuroplasticity-inspired frameworks and reversible computing, agentic AI will enhance efficiency while minimizing computational waste. Moreover, its ability to maintain ethical alignment and adaptability ensures responsible deployment across critical sectors. As agentic AI evolves, it will not only streamline problem-solving but also unlock new frontiers in scientific reasoning, driving human-centric advancements that balance precision, sustainability, and ethical integrity in the next era of discovery.