Homo agenticus in the age of agentic AI: Agency loops, power displacement, and the circulation of responsibility

Source: Paul M. Leonardi; Information and Organization (open access) · 2025

As attribution-making creatures, humans constantly seek to locate agency somewhere in their environment. Where we choose to attribute agency has profound consequences for our own sense of power, responsibility, and capacity to act. The rise of agentic AI systems disrupts these attribution processes, as humans encounter technologies that appear to act autonomously and control outcomes affecting human lives. Drawing on psychological research on apparent mental causation, I argue that agency attribution operates as an active process of power redistribution. When humans attribute agency to AI systems, they experience systematic “power displacement”—reduced sense of control and responsibility—even while retaining formal authority. This creates a paradox: the more autonomous AI appears, the less autonomous humans feel, regardless of their actual control. However, this displacement is neither permanent nor unidirectional. Agency circulates through predictable “agency loops”—recursive patterns involving delegation, attribution, contingency, reassertion, and reconfiguration—that can be strategically managed. Humans who understand how agency loops operate and intervene in them through strategic attribution processes can develop new forms of expertise and authority. The future belongs to those who understand that agency is always attributed somewhere—and that where we choose to place it determines who has power to act.

Yet for all this scholarly attention to defining, measuring, and theorizing agency, we have largely overlooked an important question: what happens when we attribute agency? While researchers continue to debate the ontological foundations of agency and seek ever more precise conceptualizations, I argue that we must shift our focus to the performative effects of agency attribution itself. This shift is crucial because the act of attributing agency does something beyond description—it actively creates the conditions for action and responsibility.

When we attribute agency to ourselves, we claim the capacity to act, accepting both the possibilities and burdens that come with such attribution. When we attribute agency to others—whether human colleagues, organizational structures, or increasingly, AI agents—we simultaneously divest ourselves of that capacity. This divestment represents a transfer of both power and responsibility. The entity to whom we attribute agency becomes, in our understanding, the locus of intentional action and the bearer of consequences. In the emerging landscape of agentic AI systems, these attributions take on heightened significance as we delegate decision-making authority to algorithmic agents, effectively ceding control over outcomes that profoundly affect human lives.

This process of attribution is neither static nor unidirectional. Instead, attributions of agency shift dynamically throughout sociotechnical systems, creating what I term “agency loops”—recursive patterns where responsibility for action circulates among human and non-human actors. A manager attributes agency to an AI recommendation system, which in turn bases its suggestions on data patterns that reflect previous human decisions, which were themselves influenced by earlier algorithmic outputs. These loops create a distributed form of power that defies traditional notions of centralized control or clear lines of accountability. Understanding agency attribution as the conferral of power reveals why questions of agency have become so pressing in the age of agentic AI. As organizations increasingly deploy AI systems that exhibit goal-directed behavior, make autonomous decisions, and adapt to changing circumstances, the attribution of agency to these systems has the potential to reshape the distribution of power. The danger lies in how our attributions of agency to these systems redistribute our own capacity for action and accountability.

This paper explores how agency loops emerge and evolve within contemporary organizational contexts, tracing the circulation of power through patterns of attribution that connect human and artificial agents. By focusing on the performative effects of agency attribution rather than its definitional boundaries, we can better understand how power is negotiated, transferred, and sometimes lost in the complex assemblages that characterize modern organizational life. In doing so, we move beyond the question of what agency is to examine what agency attribution does—and what it means for human autonomy in an age of increasingly agentic machines.

A growing body of empirical research demonstrates that when humans attribute agency to AI systems, they experience systematic displacement of power—understood as the combination of control and responsibility—from themselves to the technology. This displacement occurs even when humans retain objective authority over the systems and even when the AI systems have no actual autonomous capabilities. Ciardo et al. (2020) provide perhaps the clearest evidence of this power displacement. In their study, participants performed tasks alongside a humanoid robot named Cozmo that was presented as an intentional agent. Even when the robot never actually acted, participants reported significantly lower sense of agency over task outcomes compared to working alone.

Power displacement reveals itself most clearly when things go wrong. Jia et al. (2022) found that participants consistently credited themselves for successful outcomes when working with AI systems but blamed the AI for failures. This asymmetric attribution pattern demonstrates the motivated nature of power displacement: humans strategically attribute agency in ways that maximize their sense of control over positive outcomes while minimizing responsibility for negative ones. Yet even this strategic attribution represents a form of power displacement, as it requires acknowledging that AI systems can be causal agents capable of producing undesired effects. When humans attribute agency to AI, they actively reconstruct the distribution of power within the assemblage.

Such power displacement is dynamic rather than permanent. Most AI-human interactions follow recursive patterns that I call “agency loops”—cycles where power circulates through processes of delegation, attribution, contingency, reassertion, and reconfiguration. These loops represent the mechanism through which agency moves through sociotechnical systems, constantly redistributing responsibility and control as humans attribute agency to different entities in different circumstances. Agency loops occur because the attribution of agency to AI systems is inherently unstable. While humans readily attribute agency to AI during smooth operations, this attribution becomes problematic when systems fail, produce unexpected results, or face accountability demands. At these moments, the comfortable fiction of autonomous AI agency collapses, and humans must reassert their own agency to address consequences. This reassertion leads to reconfiguration of future attribution patterns, setting up new conditions for subsequent cycles. The cyclical nature of these interactions means that agency is always moving through a system rather than residing permanently in any single location. Power circulates through predictable patterns that shape how responsibility and control are distributed over time. Understanding these patterns reveals how complex human-AI assemblages actually operate and where opportunities for intervention might emerge.

Agency loops unfold at multiple temporal scales, from split-second interactions to multi-year organizational transformations. They can nest within each other, with micro-loops of individual correction embedded within macro-loops of institutional learning. Most importantly, they are evolutionary processes where each cycle potentially alters the conditions for subsequent interactions. The five phases of an agency loop are illustrated in Fig. 1, summarized in Table 1, and explained in detail below.

Every agency loop begins with an act of human delegation where people design, implement, configure, or deploy AI systems with specific parameters and capabilities. During this phase, humans retain maximum sense of agency because they are actively shaping the system’s possibilities and constraints. They are the architects making deliberate choices about what the AI can and cannot do.

Once AI systems begin operating within established parameters, users and observers gradually shift from experiencing them as tools to perceiving them as agents. This transition represents the critical moment where power begins to displace from humans to AI through attribution processes. Users interact with the system and experience its responses as autonomous, purposeful, and intelligent rather than as predetermined outputs of human programming. For example, AI used in radiology begins analyzing patient scans and identifying potential abnormalities. Radiologists start to experience the system as having diagnostic capabilities: “The AI flagged this suspicious area.” They begin attributing perceptual and analytical agency to the system, experiencing it as a colleague who notices things they might miss. Their language shifts from describing the system as implementing their diagnostic approach to crediting it with independent insights. Or consider AI tools used in reviewing job applicants. The AI starts screening applications and ranking candidates. Hiring managers experience the system as making judgments: “The algorithm selected these top applicants based on their qualifications.” They attribute evaluative agency to the system, experiencing it as having opinions about candidate quality rather than simply executing their predefined criteria. The system begins to feel like a hiring partner with its own assessment capabilities.

During this phase, humans experience progressively reduced sense of agency as they attribute causal power to the AI system. They begin to see outcomes as resulting from AI decisions rather than human choices. Their sense of responsibility shifts accordingly—successes and failures increasingly feel like products of AI agency rather than human judgment. This attribution requires belief that the AI acts as a causal agent producing effects in the world.

The attribution phase continues until an unexpected event disrupts the smooth operation of the system and reveals the limitations or problems with treating AI as an autonomous agent. These contingencies take various forms: technical failures, unexpected outcomes, ethical violations, legal challenges, or public controversies. They represent moments when the attribution of agency to AI systems becomes untenable or dangerous.

These contingencies shatter the illusion of autonomous AI agency and create urgent pressure for human intervention. They reveal that the comfortable delegation of agency to AI systems carries risks and responsibilities that cannot be avoided through attribution. Humans discover they cannot escape accountability by claiming the AI acted independently—they remain liable for consequences regardless of their subjective attribution patterns.

When contingencies arise, humans must dramatically reassert their agency to address consequences, provide explanations, and implement corrections. This phase involves a complete reversal of the attribution patterns that characterized the preceding phases. Humans can no longer claim the system was autonomous; they must acknowledge and exercise their authority over it.

During reassertion, humans often experience intensified sense of agency and responsibility as they work to address consequences of their previous delegation. They must exercise explicit control, make visible decisions, and accept accountability for both the AI’s past actions and their own corrective measures. This phase often involves significant stress as humans confront the gap between their subjective experience of having delegated agency and their objective responsibility for outcomes.

Based on their experience with contingency and reassertion, humans adjust their delegation practices and attribution patterns for future interactions with AI systems. This reconfiguration phase sets new conditions for subsequent loops, potentially altering how agency will be attributed and power will be distributed in future cycles.

Agency loops are recursive systems that produce cumulative change. Each cycle through the loop—delegation, attribution, contingency, reassertion, and reconfiguration—shapes the next. Delegation establishes technical constraints and implicit expectations about what the AI system should do. Attribution defines how agency is distributed and signals how much responsibility humans are willing to transfer. Contingency reveals how those attributions hold up under pressure. Reassertion is a return to human control that forces clarification of roles and consequences. Reconfiguration sets new conditions for interaction, encoding lessons into modified practices, altered oversight structures, or revised system parameters. As humans move through these cycles, they gradually rework how they relate to AI systems. Power and responsibility shift, stabilize, or fracture based on what happens during each loop. Over time, these shifts create new organizational patterns that reflect the history of these recursive adjustments.

The comparative analysis of these three cases reveals several insights about how agency loops operate and what determines their evolutionary trajectories. Most importantly, these cases demonstrate that agency loops follow predictable patterns that can be understood and potentially guided toward beneficial outcomes. Table 3 provides a systematic comparison of how the five-phase agency loop framework manifested across three distinct organizational contexts—radiological diagnosis, AI scheduling assistants, and police predictive analytics. While each case involved sophisticated AI systems with genuine analytical capabilities, they produced dramatically different outcomes: distributed diagnostic agency, managed autonomy, and curatorial authority, respectively. By examining these cases through the same analytical dimensions—from initial delegation logic through final power distribution—the table reveals both the universal structure of agency loops and the context-specific factors that shape their evolutionary trajectories. This comparative view demonstrates that successful navigation of agency loops depends less on the technical sophistication of AI systems than on how humans strategically manage attribution processes, develop appropriate intervention practices, and reconfigure power relationships over time. Four key insights emerge from this cross-case analysis that have important implications for both theory and practice.

Perhaps most surprisingly, all three cases demonstrate that humans can actually enhance their power and influence through strategic management of agency attribution rather than simply losing control to AI systems. In each case, the final configuration involved humans gaining new forms of authority that were unavailable before AI implementation. Radiologists gained authority as expert interpreters who could integrate AI insights with clinical knowledge in ways that neither humans nor AI could achieve independently. Scheduling principals gained authority as social mediators who could manage complex human-AI relationships and navigate impression management challenges. Police officers gained authority as knowledge curators who could shape organizational understanding while maintaining technological legitimacy. This pattern challenges common assumptions about AI implementation leading to human disempowerment. Instead, the cases suggest that thoughtful engagement with agency attribution processes can create opportunities for humans to develop new forms of expertise and influence that enhance rather than diminish their agency.

Finally, the cases reveal that agency loops exhibit strong path dependence—early experiences with agency attribution shape subsequent patterns in ways that can lock human-AI relationships into particular trajectories. In radiology, early experiences of AI identifying missed pathology created conditions for productive interrogation practices. In scheduling, early experiences of social delegation created conditions for ongoing mediation strategies. In policing, early experiences of opacity created conditions for substitution approaches. This path dependence means that the early phases of agency loops are particularly crucial for determining long-term outcomes. Organizations cannot simply deploy AI systems and expect optimal human-AI relationships to emerge naturally—they must actively guide early attribution processes toward beneficial patterns.

This paper has introduced three interconnected concepts that challenge how organizational theory and information systems research approaches human-AI relations: agency loops, power displacement, and the circulation of responsibility. Together, these concepts reveal that the relationship between humans and AI systems is far more dynamic and complex than existing theories suggest, requiring us to move beyond static conceptions of agency that have dominated organizational studies of technology.

Power displacement through agency attribution represents the mechanism through which human-AI relationships are negotiated. When humans attribute agency to AI systems, they experience systematic reduction in their own sense of control and responsibility, even when they retain formal authority (Berberian et al., 2012; Ciardo et al., 2020). This displacement is an active reconstruction of causal relationships that redistributes power throughout sociotechnical systems—a process that existing IS theories of agency have not adequately captured. Importantly, this displacement is neither permanent nor unidirectional. The concept of agency loops reveals that power displacement operates as part of a cyclical process—delegation, attribution, contingency, reassertion, and reconfiguration—where responsibility and control circulate through human-AI assemblages rather than simply transferring from humans to machines. This circulation challenges the linear progression from technological determinism to social construction that has characterized much IS theorizing about agency (see for review, Leonardi & Barley, 2010), revealing instead recursive patterns where technology and social practices continuously reshape each other.

The circulation of responsibility emerges as the connecting thread that links power displacement to agency loops. Responsibility flows through attribution processes that constantly redistribute accountability across the assemblage rather than simply residing in human decision-makers or AI systems. This circulation creates opportunities for strategic management of responsibility—humans can temporarily cede control in some domains while maintaining or enhancing it in others, developing new forms of influence through their ability to shape how responsibility flows through the system.

The temporary and strategic nature of power displacement also challenges zero-sum assumptions about human-AI relationships that have influenced both popular discourse and academic research. Rather than humans simply “losing power” to AI systems, power displacement reveals a more complex process where control and responsibility are redistributed in ways that can enhance the capacities of both humans and AI systems. This provides a more optimistic perspective on human-AI collaboration than either technological determinism or resistance-focused social construction approaches would suggest, aligning with recent work showing how humans can gain new forms of authority through strategic engagement with AI systems (Endacott & Leonardi, 2022; Raisch & Krakowski, 2021).

Understanding responsibility circulation also reveals why traditional governance mechanisms may be inadequate for managing AI systems, extending critiques of technological governance that have emerged in technology research (Beane & Leonardi, 2025; Pasquale, 2015; Zuboff, 2019). Approaches that attempt to fix responsibility in particular locations may be undermined by attribution processes that continuously redistribute accountability. More effective governance may require developing institutional mechanisms that can work with rather than against responsibility circulation, creating stable frameworks for accountability within dynamic attribution processes.

It is tempting to say that as homo agenticus, we seek to attribute agency. But as I have argued, it is more appropriate to say that as homo agenticus we create agency and displace power through attribution. The framework of agency loops demonstrates that our relationship with AI systems is not a zero-sum battle for control but an ongoing negotiation over the circulation of responsibility and power—a negotiation we can learn to conduct with intention and skill. The empirical evidence from radiology, scheduling, and policing shows that when humans understand attribution as an active process rather than a passive recognition, they can guide agency loops toward configurations that enhance human authority while leveraging AI capabilities. The organizations and individuals who will thrive are those who recognize that agency attribution is perhaps the most consequential skill of our technological age. In mastering this skill, we do not simply adapt to AI—we actively construct the conditions under which human agency can flourish alongside artificial intelligence, ensuring that our relentless pursuit of control serves not just our survival but our continued evolution as fundamentally agentic beings.