Recent advancements in artificial intelligence (AI) have catalyzed the emergence of agentic information systems (IS), which exhibit autonomous behavior and advanced cognitive capabilities. Unlike traditional IS, which functioned primarily as reactive tools supporting humans, agentic IS can make decisions independently, act in unstructured environments, and even delegate tasks to humans. This paradigm shift fundamentally transforms the human-IS relationship, questioning the long-standing assumption of human agentic primacy in IS research and practice. In this article, we provide a conceptual overview of agentic IS, delineating their defining characteristics and situating them within the broader evolution of IS. We introduce key archetypes of agentic IS, explore novel patterns of delegation and interaction between humans and machines, and discuss the socio-technical implications of these developments. Furthermore, we highlight the challenges and risks associated with integrating agentic IS from an individual, organizational, and societal perspective, emphasizing the need for nuanced understanding to harness the potential while addressing emerging complexities.

The rise of generative AI and large language models (LLMs) has sparked interest in applying generative agents to simulate users in conversational recommender system (CRS) evaluations. While CRS rely on user perception for success, traditional user studies are costly and time-intensive. Existing simulation approaches lack interaction depth and user-centric evaluation. This study addresses these gaps by leveraging LLM-based generative agents to conduct scalable, subjective CRS assessments. We present a comparative study where 200 generative agents and 50 human participants interact with a prototypical agentic CRS and evaluate their experience using a structured questionnaire. Results indicate that generative agents approximate human-like behavior and subjective assessments at the macro-level, despite granular precision limitations, offering an alternative to traditional user studies. Our findings advance research on CRS evaluation by demonstrating how agentic simulations can support human-aligned assessments of socio-technical systems and open new avenues for applying Generative AI in user-centered decision support.

The evolution of information systems (IS) in recent years, driven by advancements such as AI-based digital assistants and autonomous vehicles, has fundamentally changed human-IS interaction. This transformation extends beyond mere user interaction to encompass the emergence of agentic IS, challenging traditional notions of human supervision. It prompts a reevaluation of IS research, with a focus on IS delegation as a new theoretical lens. However, research remains nascent, particularly in understanding the factors influencing delegation decisions. We compiled 26 articles and derived six key delegation mechanisms and their influencing factors, using grounded theory and co-occurrence analysis. We investigate attributes of human and the agentic IS, as well as task attributes and the delegation level that are relevant to the delegation mechanisms. Our research provides a nuanced understanding of factors influencing delegation decisions to agentic IS. We call for research considering the yet underrepresented but crucial interdependencies with a research agenda.