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

Agentic workflows represent a transformative approach to AI, integrating AI agents into dynamic processes that combine reasoning, tool utilization, and memory to accomplish complex tasks with limited human intervention. AI agents, equipped with large language models (LLMs), can plan, execute, and refine tasks by breaking them down into manageable steps, dynamically selecting tools for real-time interactions, and learning from past experiences to enhance performance. This adaptability is showcased in agentic workflows like Retrieval-Augmented Generation (RAG) and agentic research assistants, which offer deep insights and decision-making capabilities beyond static models. Despite their benefits, agentic workflows introduce challenges such as potential complexity for simple tasks and ethical concerns over autonomy. By understanding the components and patterns of agentic workflows, such as planning, tool use, and reflection, organizations can effectively leverage this technology, while being mindful of its limitations and ensuring responsible deployment.