Niclas Flehmig
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Niclas Flehmig has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
Mary Ann Lundteigen
1 shared publication- Mary Ann Lundteigen
Norwegian University of Science and Technology
1 shared publication Shen Yin
1 shared publication- Shen Yin
Department of Mechanical and Industrial Engineering, Faculty of Engineering, Norwegian University of Science and Technology, Trondheim, Norway
1 shared publication
Latest Publications
- 2 July 2026
- Published by Springer Nature
- p. 231-249
Perspectives on a Reliability Monitoring Framework for Agentic AI Systems
- 2 July 2026
- Published by Springer Nature
- p. 231-249
Perspectives on a Reliability Monitoring Framework for Agentic AI Systems
- Open Access
Perspectives on a Reliability Monitoring Framework for Agentic AI Systems
The implementation of agentic AI systems has the potential of providing more helpful AI systems in a variety of applications. These systems work autonomously towards a defined goal with reduced external control. Despite their potential, one of their flaws is the insufficient reliability which makes them especially unsuitable for high-risk domains such as healthcare or process industry. Unreliable systems pose a risk in terms of unexpected behavior during operation and mitigation techniques are needed. In this work, we derive the main reliability challenges of agentic AI systems during operation based on their characteristics. We draw the connection to traditional AI systems and formulate a fundamental reliability challenge during operation which is inherent to traditional and agentic AI systems. As our main contribution, we propose a two-layered reliability monitoring framework for agentic AI systems which consists of a out-of-distribution detection layer for novel inputs and AI transparency layer to reveal internal operations. This two-layered monitoring approach gives a human operator the decision support which is needed to decide whether an output is potential unreliable or not and intervene. This framework provides a foundation for developing mitigation techniques to reduce risk stemming from uncertain reliability during operation.
Open AccessPerspectives on a Reliability Monitoring Framework for Agentic AI Systems
The implementation of agentic AI systems has the potential of providing more helpful AI systems in a variety of applications. These systems work autonomously towards a defined goal with reduced external control. Despite their potential, one of their flaws is the insufficient reliability which makes them especially unsuitable for high-risk domains such as healthcare or process industry. Unreliable systems pose a risk in terms of unexpected behavior during operation and mitigation techniques are needed. In this work, we derive the main reliability challenges of agentic AI systems during operation based on their characteristics. We draw the connection to traditional AI systems and formulate a fundamental reliability challenge during operation which is inherent to traditional and agentic AI systems. As our main contribution, we propose a two-layered reliability monitoring framework for agentic AI systems which consists of a out-of-distribution detection layer for novel inputs and AI transparency layer to reveal internal operations. This two-layered monitoring approach gives a human operator the decision support which is needed to decide whether an output is potential unreliable or not and intervene. This framework provides a foundation for developing mitigation techniques to reduce risk stemming from uncertain reliability during operation.