Learn about AFLOW, a groundbreaking framework for automating and optimizing agentic workflows in large language models (LLMs), in this technical research presentation. Explore how AFLOW reframes workflow construction as a code-based search problem, utilizing graph representations where nodes correspond to LLM invocations and edges represent logical flow. Discover the framework's innovative approach to workflow optimization through specialized Monte Carlo Tree Search algorithms, LLM-driven node expansion, and predefined operators that encapsulate common agentic operations. Understand the technical implementation details, from the soft mixed-probability selection mechanism to experience backpropagation, and see how AFLOW achieves a 5.7% performance improvement over existing baselines while enabling smaller models to match GPT-4's capabilities at reduced computational costs. Delve into the distinctions between autonomous AI agents and agentic workflows, explore various workflow types, and learn how this framework can significantly reduce human intervention, resource requirements, and operational costs in AI systems. Read more