This 15-minute video from Discover AI explores how Action Planning via In-Context Learning (ICL) can optimize large language model planning capabilities. Learn how planning mechanisms enable AI systems to proactively determine action sequences for achieving objectives in both virtual and real-world environments. Discover why ICL represents a powerful instrument for improving action sequence planning in agentic systems. The content is based on research titled "Improving Large Language Model Planning with Action Sequence Similarity" by researchers from Google DeepMind and Carnegie Mellon University, covering topics in AI reasoning, AI agents, logical reasoning, and AI learning methodologies.