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.
Table of contents
What are AI agents? What are Agentic Workflows? Patterns in Agentic Workflows Agentic Workflows Use Cases Agentic Workflow Examples Benefits and Limitations of Agentic Workflows Summary Ready to start building? Don't want to miss another blog post?Sort: