Agentic Business Process Management: The Past 30 Years And Practitioners' Future Perspectives

• URL: http://arxiv.org/abs/2504.03693v1
• Date: Sun, 23 Mar 2025 20:15:24 GMT
• Title: Agentic Business Process Management: The Past 30 Years And Practitioners' Future Perspectives
• Authors: Hoang Vu, Nataliia Klievtsova, Henrik Leopold, Stefanie Rinderle-Ma, Timotheus Kampik,
• Abstract summary: We conduct a series of interviews with BPM practitioners to explore their understanding, expectations, and concerns related to agent autonomy, adaptability, human collaboration, and governance in processes.<n>The findings reflect both challenges with respect to data inconsistencies, manual interventions, identification of process bottlenecks, actionability of process improvements, as well as the opportunities of enhanced efficiency, predictive process insights and proactive decision-making support.<n>These concerns underscore the need for a robust methodological framework for managing agents in organizations.
• Score: 0.7270112855088837
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the advent of generative Artificial Intelligence (genAI), the notion of an agent has seen a resurgence in popularity. This has also led to speculation about the extent to which business process management, as a discipline and research field, may impact and be impacted by the deployment of genAI-based agents. To better ground such speculations into the state-of-the-art, we draw from the past 30 years of research on agents and business process management to establish the concept of Agentic Business Process Management (agentic BPM) that is only loosely coupled to the genAI hype. We conduct a series of interviews with BPM practitioners to explore their understanding, expectations, and concerns related to agent autonomy, adaptability, human collaboration, and governance in processes. The findings reflect both challenges with respect to data inconsistencies, manual interventions, identification of process bottlenecks, actionability of process improvements, as well as the opportunities of enhanced efficiency, predictive process insights and proactive decision-making support. While the technology offers potential benefits, practitioners also anticipate risks such as biases, over-reliance, lack of transparency, and job displacement within organizations. These concerns underscore the need for a robust methodological framework for managing agents in organizations.

Related papers

• A Desideratum for Conversational Agents: Capabilities, Challenges, and Future Directions [51.96890647837277]
  Large Language Models (LLMs) have propelled conversational AI from traditional dialogue systems into sophisticated agents capable of autonomous actions, contextual awareness, and multi-turn interactions with users. This survey paper presents a desideratum for next-generation Conversational Agents - what has been achieved, what challenges persist, and what must be done for more scalable systems that approach human-level intelligence.
  arXiv  Detail & Related papers  (2025-04-07T21:01:25Z)
• Agentic Knowledgeable Self-awareness [79.25908923383776]
  KnowSelf is a data-centric approach that applies agents with knowledgeable self-awareness like humans.<n>Our experiments demonstrate that KnowSelf can outperform various strong baselines on different tasks and models with minimal use of external knowledge.
  arXiv  Detail & Related papers  (2025-04-04T16:03:38Z)
• Inherent and emergent liability issues in LLM-based agentic systems: a principal-agent perspective [0.0]
  Agentic systems powered by large language models (LLMs) are becoming progressively more complex and capable. Their increasing agency and expanding deployment settings attract growing attention over effective governance policies, monitoring and control protocols. We analyze the potential liability issues stemming from delegated use of LLM agents and their extended systems from a principal-agent perspective.
  arXiv  Detail & Related papers  (2025-04-04T08:10:02Z)
• Media and responsible AI governance: a game-theoretic and LLM analysis [61.132523071109354]
  This paper investigates the interplay between AI developers, regulators, users, and the media in fostering trustworthy AI systems.<n>Using evolutionary game theory and large language models (LLMs), we model the strategic interactions among these actors under different regulatory regimes.
  arXiv  Detail & Related papers  (2025-03-12T21:39:38Z)
• Human Decision-making is Susceptible to AI-driven Manipulation [87.24007555151452]
  AI systems may exploit users' cognitive biases and emotional vulnerabilities to steer them toward harmful outcomes. This study examined human susceptibility to such manipulation in financial and emotional decision-making contexts.
  arXiv  Detail & Related papers  (2025-02-11T15:56:22Z)
• AgentOps: Enabling Observability of LLM Agents [12.49728300301026]
  Large language model (LLM) agents raise significant concerns on AI safety due to their autonomous and non-deterministic behavior. We present a comprehensive taxonomy of AgentOps, identifying the artifacts and associated data that should be traced throughout the entire lifecycle of agents to achieve effective observability. Our taxonomy serves as a reference template for developers to design and implement AgentOps infrastructure that supports monitoring, logging, and analytics.
  arXiv  Detail & Related papers  (2024-11-08T02:31:03Z)
• Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance [95.03771007780976]
  We tackle the challenge of developing proactive agents capable of anticipating and initiating tasks without explicit human instructions.<n>First, we collect real-world human activities to generate proactive task predictions.<n>These predictions are labeled by human annotators as either accepted or rejected.<n>The labeled data is used to train a reward model that simulates human judgment.
  arXiv  Detail & Related papers  (2024-10-16T08:24:09Z)
• Visibility into AI Agents [9.067567737098594]
  Increased delegation of commercial, scientific, governmental, and personal activities to AI agents may exacerbate existing societal risks. We assess three categories of measures to increase visibility into AI agents: agent identifiers, real-time monitoring, and activity logging.
  arXiv  Detail & Related papers  (2024-01-23T23:18:33Z)
• Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies [79.60322329952453]
  We show how to develop interpretable representations of how agents make decisions. By understanding the decision-making processes underlying a set of observed trajectories, we cast the policy inference problem as the inverse to this online learning problem. We introduce a practical algorithm for retrospectively estimating such perceived effects, alongside the process through which agents update them. Through application to the analysis of UNOS organ donation acceptance decisions, we demonstrate that our approach can bring valuable insights into the factors that govern decision processes and how they change over time.
  arXiv  Detail & Related papers  (2022-03-14T17:40:42Z)
• Building Affordance Relations for Robotic Agents - A Review [7.50722199393581]
  Affordances describe the possibilities for an agent to perform actions with an object. We review and find common ground amongst different strategies that use the concept of affordances within robotic tasks. We identify and discuss a range of interesting research directions involving affordances that have the potential to improve the capabilities of an AI agent.
  arXiv  Detail & Related papers  (2021-05-14T08:35:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.