Query: elaborate this article with 20 referrence:
Agentic AI workflows refer to the use of artificial intelligence (AI) agents to automate tasks and workflows, enabling humans to focus on higher-level decision-making and strategy. The concept of agentic AI workflows is an emerging area of research that has gained significant attention in recent years.

One of the key benefits of agentic AI workflows is their ability to automate tasks that require human-like intelligence, such as decision-making and problem-solving (Ye et al. 20231). Agentic AI workflows can be used to construct and execute workflows, allowing AI agents to take on tasks that were previously performed by humans (Ye et al. 20231). This can lead to increased efficiency and productivity, as well as improved accuracy and consistency (Ye et al. 20231).

Recent work has proposed the use of Large Language Models (LLMs) to construct agentic workflows (Zhang et al. 2024). LLMs have been shown to demonstrate human-like intelligence and can be used to automate tasks that require complex decision-making (Ye et al. 20232). The use of LLMs in agentic workflows has been explored in various domains, including data analysis (Zhang et al. 2024), question answering (Zhang et al. 2024), and software development (Ye et al. 20232).

Agentic AI workflows have also been used to automate tasks in industries such as healthcare (Fleurence et al. 2024), finance (Ye et al. 20234), and manufacturing (Ye et al. 20233). In these industries, agentic AI workflows can be used to automate tasks such as data analysis, decision-making, and process optimization (Ye et al. 20234)(Fleurence et al. 2024).

Furthermore, agentic AI workflows can be used to automate tasks that require collaboration between multiple agents (Mitra et al. 2024). For example, multi-agent workflows can be used to simulate scenarios and generate new prompts and responses (Mitra et al. 2024). This can be particularly useful in industries such as customer service, where agents need to respond to a wide range of customer inquiries (Mitra et al. 2024).

However, the use of agentic AI workflows also raises concerns about privacy and security (Ye et al. 20234). As AI agents become more pervasive in industries, there is a growing need to ensure that they are designed and deployed in a way that protects user data and prevents unauthorized access (Ye et al. 20234).

In conclusion, agentic AI workflows have the potential to unlock the true potential of AI by automating tasks that require human-like intelligence (Ye et al. 20231). However, it is important to address the challenges and concerns associated with the use of agentic AI workflows, including privacy and security (Ye et al. 20234).

References
Yining Ye, Xin Cong, Shizuo Tian, Jian Cao, Hao Wang, Yujia Qin et al. ProAgent: From Robotic Process Automation to Agentic Process Automation. 2023. arXiv.org.
Arindam Mitra, Luciano Del Corro, Guoqing Zheng, Shweti Mahajan, Dany Rouhana, Andres Codas et al. AgentInstruct: Toward Generative Teaching with Agentic Flows. 2024. arXiv.org.
Jiayi Zhang, Jinyu Xiang, Zhaoyang Yu, Fengwei Teng, Xionghui Chen, Jiaqi Chen et al. AFlow: Automating Agentic Workflow Generation. 2024. arXiv.org.
Rachael Fleurence, Xiaoyan Wang, Jiang Bian, Mitch Higashi, Turgay Ayer, Hua Xu et al. Generative AI in Health Economics and Outcomes Research: A Taxonomy of Key Definitions and Emerging Applications, an ISPOR Working Group Report. 2024. .
Subject: computer science
Intent: Literature Understanding