Agentic Business Process Management: A GenAI‑Driven Transformation
- OUS Academy in Switzerland
- Jul 21
- 3 min read
Author: Samuel Lewis
Affiliation: Independent researcher
Abstract
Agentic Business Process Management (Agentic BPM) represents a paradigm shift in organizational operations. Drawing upon three decades of foundational BPM research, this article introduces Agentic BPM—a discipline in which generative AI agents autonomously drive process execution, monitor performance, and dynamically adapt workflows. We explore historical context, system architecture, practitioner perspectives, benefits, and ethical implications. This analysis draws from recent practitioner interviews and current literature, offering a high‑level roadmap for future research and implementation.
1. Introduction
Business Process Management (BPM) traditionally formalized procedures through human-defined workflows. With the rise of generative AI (genAI), organizations are exploring Agentic BPM, where autonomous agents manage end‑to‑end processes. This article synthesizes historical developments, practical implications, and future directions for this emerging approach.
2. Historical Evolution of BPM and Agents
Over the past 30 years, BPM research cultivated tools for modeling, executing, and optimizing workflows. Initially manual and static, these systems gradually transitioned to dynamic, data‑driven platforms.
Concurrently, agent research—spanning intelligent software entities capable of perception, reasoning, and action—developed theoretical foundations for autonomy and inter-agent collaboration. The convergence of BPM and agentic principles built a foundation for Agentic BPM.
3. Defining Agentic BPM
Agentic BPM refers to process management systems that delegate process tasks to autonomous or semi-autonomous agents empowered by genAI. Core characteristics include:
Autonomy: Agents act independently based on goals and rules.
Adaptability: Agents sense and respond to changing data and context.
Collaboration: Agents and humans coordinate in hybrid workflows.
Governance: Human-defined policies regulate agent behavior.
This framework reconceptualizes agents not merely as tools but as full partners within business workflows.
4. Practitioner Insights
Recent interviews with BPM professionals offer rich insights:
Efficiency Gains: Agents accelerate monitoring and response in real time.
Predictive Insights: AI supports proactive decision-making.
Challenges: Data consistency issues, transparency, and human trust were noted.
Concerns: Risk of bias, overreliance, lack of explainability, and job displacement.
These findings highlight both promise and caution as firms pilot Agentic BPM.
5. Conceptual Framework
We propose a four-tier model:
Sensing Layer: Agents collect real-time inputs from systems.
Analysis Layer: GenAI analyzes data using ML and natural language.
Execution Layer: Agents trigger actions like task assignments or system adjustments.
Governance Layer: Humans define guardrails through policies and audits.
This multi-layered system combines agile responsiveness with oversight to maintain accountability.
6. Benefits of Agentic BPM
Operational Efficiency: Agents reduce cycle times.
Scalability: Multipurpose agents support expanding workflows.
Resilience: Dynamic agents respond swiftly to disruptions.
Insight Generation: AI-enhanced data fosters process innovation.
Human Focus: Employees move from routine tasks to strategic roles.
7. Risks and Governance
Transitioning to Agentic BPM introduces new risks:
Bias & Fairness: AI agents may learn and replicate biases.
Opacity: Decision-making becomes less transparent.
Reliability: Errors by agents may influence outcomes.
Labor Impact: Worker roles may be reshaped, requiring re-skilling.
To address these, we recommend:
Algorithmic transparency.
Regular audits and evaluation.
Inclusive training programs.
Policy frameworks ensuring human oversight.
8. Case Illustration
A manufacturing firm deploys agents to oversee quality assurance. Agents analyze real-time sensor data, detect anomalies like temperature spikes, and automatically initiate containment protocols—alerting managers as needed. This significantly reduces defects and accelerates incident response.
9. Research Agenda
Future studies should explore:
Governance Models: Policy designs enabling responsible agent autonomy.
Hybrid Teams: Optimal integration of human and AI roles.
Ethics & Explainability: Frameworks for traceability and fairness.
Measuring Impact: Quantitative studies on efficiency and human effects.
Domain-specific Applications: Agentic BPM in finance, healthcare, logistics.
10. Conclusion
Agentic BPM offers a bold vision of autonomous, intelligent, and adaptive organizational processes. By integrating genAI agents with robust governance, firms can achieve operational excellence and human-centric innovation. Yet, realizing its full potential demands careful attention to bias, transparency, and workforce transformation. Agentic BPM marks a transformative step in BPM scholarship and practice.
References
Vu, H., Klievtsova, N., Leopold, H., Rinderle‑Ma, S., & Kampik, T. (2025). Agentic Business Process Management: The Past 30 Years And Practitioners’ Future Perspectives. arXiv.
Shrestha, Y. R., Krishna, V., & von Krogh, G. (2020). Augmenting Organizational Decision‑Making with Deep Learning Algorithms: Principles, Promises, and Challenges. arXiv.
Kerstens, A. & Langley, D. J. (2025). “An Innovation Intermediary’s Role in Enhancing Absorptive Capacity for Cross‑Industry Digital Innovation: Introducing an Awareness Capability and New Intermediary Practices.” Journal of Business Research.
Mahajan, N. (2025). “Augmented Intelligence in Program Management: Enhancing Human Leadership with AI.” PM World Journal.
MIT Sloan Management Review. (2025). “Why Robots Will Displace Managers — and Create Other Jobs.”
