Visioning Human-Agentic AI Teaming: Continuity, Tension, and Future Research
arXiv:2603.04746v1 Announce Type: new Abstract: Artificial intelligence is undergoing a structural transformation marked by the rise of agentic systems capable of open-ended action trajectories, generative representations and outputs, and evolving objectives. These properties introduce structural uncertainty into human-AI teaming (HAT), including uncertainty about behavior trajectories, epistemic grounding, and the stability of governing logics over time. Under such conditions, alignment cannot be secured through agreement on bounded outputs; it must be continuously sustained as plans unfold and priorities shift. We advance Team Situation Awareness (Team SA) theory, grounded in shared perception, comprehension, and projection, as an integrative anchor for this transition. While Team SA remains analytically foundational, its stabilizing logic presumes that shared awareness, once achieved, will support coordinated action through iterative updating. Agentic AI challenges this presumption
arXiv:2603.04746v1 Announce Type: new Abstract: Artificial intelligence is undergoing a structural transformation marked by the rise of agentic systems capable of open-ended action trajectories, generative representations and outputs, and evolving objectives. These properties introduce structural uncertainty into human-AI teaming (HAT), including uncertainty about behavior trajectories, epistemic grounding, and the stability of governing logics over time. Under such conditions, alignment cannot be secured through agreement on bounded outputs; it must be continuously sustained as plans unfold and priorities shift. We advance Team Situation Awareness (Team SA) theory, grounded in shared perception, comprehension, and projection, as an integrative anchor for this transition. While Team SA remains analytically foundational, its stabilizing logic presumes that shared awareness, once achieved, will support coordinated action through iterative updating. Agentic AI challenges this presumption. Our argument unfolds in two stages: first, we extend Team SA to reconceptualize both human and AI awareness under open-ended agency, including the sensemaking of projection congruence across heterogeneous systems. Second, we interrogate whether the dynamic processes traditionally assumed to stabilize teaming in relational interaction, cognitive learning, and coordination and control continue to function under adaptive autonomy. By distinguishing continuity from tension, we clarify where foundational insights hold and where structural uncertainty introduces strain, and articulate a forward-looking research agenda for HAT. The central challenge of HAT is not whether humans and AI can agree in the moment, but whether they can remain aligned as futures are continuously generated, revised, enacted, and governed over time.
Executive Summary
This article addresses the challenges of human-AI teaming (HAT) in the context of increasingly agentic artificial intelligence (AI) systems. The authors argue that traditional theories of human-AI collaboration, such as Team Situation Awareness (Team SA), are insufficient to handle the structural uncertainty introduced by open-ended AI agency. The authors extend Team SA to incorporate agentic AI awareness and interrogate the dynamics of relational interaction, cognitive learning, and coordination and control under adaptive autonomy. By distinguishing continuity from tension, the authors clarify where foundational insights hold and where structural uncertainty introduces strain, and articulate a forward-looking research agenda for HAT.
Key Points
- ▸ Agentic AI introduces structural uncertainty into human-AI teaming (HAT) due to open-ended action trajectories, generative representations, and evolving objectives.
- ▸ Traditional theories of HAT, such as Team Situation Awareness (Team SA), are insufficient to handle the structural uncertainty introduced by agentic AI.
- ▸ Team SA needs to be extended to incorporate agentic AI awareness and interrogate the dynamics of relational interaction, cognitive learning, and coordination and control under adaptive autonomy.
Merits
Strength
The article provides a comprehensive analysis of the challenges of HAT in the context of agentic AI and articulates a forward-looking research agenda for the field.
Strength
The authors' extension of Team SA to incorporate agentic AI awareness is a significant contribution to the field and highlights the need for new theoretical frameworks to handle the structural uncertainty introduced by open-ended AI agency.
Strength
The article's focus on the dynamics of relational interaction, cognitive learning, and coordination and control under adaptive autonomy is a timely and important contribution to the field of HAT.
Demerits
Limitation
The article assumes a high level of technical expertise in AI and HAT, which may limit its accessibility to a broader audience.
Limitation
The article's focus on the theoretical challenges of HAT may lead to a lack of practical applications and case studies to illustrate the concepts and ideas presented.
Expert Commentary
This article makes a significant contribution to the field of human-AI teaming (HAT) by providing a comprehensive analysis of the challenges of HAT in the context of agentic AI. The authors' extension of Team Situation Awareness (Team SA) to incorporate agentic AI awareness is a significant contribution to the field and highlights the need for new theoretical frameworks to handle the structural uncertainty introduced by open-ended AI agency. The article's focus on the dynamics of relational interaction, cognitive learning, and coordination and control under adaptive autonomy is also a timely and important contribution to the field. However, the article assumes a high level of technical expertise in AI and HAT, which may limit its accessibility to a broader audience. Additionally, the article's focus on the theoretical challenges of HAT may lead to a lack of practical applications and case studies to illustrate the concepts and ideas presented.
Recommendations
- ✓ Recommendation 1: Researchers and practitioners in the field of HAT should prioritize the development of new theoretical frameworks and models that can handle the structural uncertainty introduced by agentic AI.
- ✓ Recommendation 2: Policymakers and regulators should develop new policies and regulations that address the challenges of HAT in the context of agentic AI, including issues related to transparency, accountability, and explainability.
