Agentic workflows give AI agents structure, purpose, and adaptability. This article breaks down their components, patterns, and practical applications.

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Agentic workflows enhance AI agents by providing structure and adaptability, allowing them to plan, execute tasks with tools, and learn from past experiences. This process makes them effective in tackling complex tasks across various domains. Core components like reasoning, tool use, and memory define these workflows. Use cases such as agentic RAG and research assistants demonstrate the practical application of agentic workflows, while various patterns like planning and reflection help in optimizing performance. Despite their benefits, agentic workflows also come with challenges, including potential complexity and the need for careful oversight.

What Are Agentic Workflows? Patterns, Use Cases, Examples, and More

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