Human-AI Collaboration in Agile TeamsXP 2026

Agile teams are rapidly adopting AI assistants, copilots, and autonomous agents across planning, designing, coding, testing, documentation, and analytics. These augmented teams promise higher productivity and faster feedback but also bring new coordination challenges, evolving roles, and questions about optimal team size, team autonomy, required competencies, and trust between humans and AI agents.

This research–practice workshop explores how AI affects everyday agile activities and how teams can intentionally integrate, govern, and collaborate. A key theme is continuous learning, how both humans and AI agents adapt together through feedback, experimentation, and reflection.

We invite short research papers, industry experience reports, and position papers addressing topics such as human–AI team configurations and routines, competence and role development, onboarding and offboarding of AI teammates, feedback loops, transparency, and evaluation in agile contexts.

The workshop combines short presentations with interactive group discussions. Together, participants will identify emerging challenges and promising practices and co-create a shared research agenda for the XP community and for future empirical studies. Selected revised papers meeting quality standards will appear in the XP Workshops post-proceedings.

Call for Submissions

XP 2026 Workshop on Human–AI Collaboration in Agile Teams

AI assistants, copilots, and autonomous agents are rapidly becoming part of everyday work in agile teams—supporting planning, design, coding, testing, documentation, and analytics. While teams and team members often report faster workflows and perceived productivity gains, AI also introduces new socio-technical challenges: shifting roles, coordination overhead, evolving competencies, trust, team autonomy, quality risks, governance needs, and uncertainty about how humans and AI should collaborate.

Building on the XP community’s long-standing interest in team autonomy and socio-technical systems (including the earlier Autonomous-teams workshops at XP), this workshop extends those discussions into a new domain: how agile teams integrate AI assistants and agents as part of their work practices. As with autonomous agile teams, simply adding AI to existing structures does not guarantee better performance. Teams must learn how to learn with AI, adapt routines, change existing workflows, align decision-making, and manage the increasing heterogeneity introduced by AI teammates.

Human–AI Collaboration in Agile Teams is a research–practice workshop that explores how AI affects everyday agile activities and how teams can intentionally integrate, coordinate, govern, and improve collaboration with AI assistants. We welcome empirical studies, experience reports, conceptual papers, and position papers that help the XP community understand how AI reshapes agile teamwork.

The workshop blends short presentations with highly interactive group activities and will conclude with a collaboratively developed research agenda. Selected revised papers will be invited to appear in the XP Workshops post-proceedings.

Topics of Interest include, but are not limited to:

Human–AI Collaboration and Teamwork

• Human–AI teaming patterns and socio-technical dynamics
• Communication and coordination between humans and AI agents
• Trust, transparency, explainability, and supervision of AI teammates
• Feedback loops and continuous learning in human–AI teams

Team Structures, Autonomy, and Alignment

• How AI changes roles, work processes, routines, authority, and decision-making
• Balancing team autonomy, organizational alignment, and AI autonomy
• Governance models for AI assistants in agile teams
• How to scale AI-augmented teamwork in a large-scale organization.
• Architectural and organizational structures that support human–AI collaboration

Competence and Learning

• Evolving skills and competencies for AI-augmented teamwork
• Onboarding and offboarding AI agents in teams
• Heterogeneous teams (e.g., BizDevOps + AI) and performance variability
• Knowledge sharing in AI-enabled teams

Evaluation and Impact

• Measuring productivity, quality, and collaboration when AI is involved
• Understanding when AI enhances teamwork—and when it disrupts it
• Ethical considerations and risks in AI-supported team processes

Submission Types

All submissions will undergo peer review. Selected, revised papers will be published in the XP Workshops post-proceedings. We invite contributions in two categories:

1. Full Papers (8 pages)

We welcome submissions representing all stages of research, including:

• position papers
• work-in-progress
• completed research studies
• Industry experience reports

Full papers must use the Springer LNCS/LNBIP conference proceedings format (templates for LaTeX and Word are available here: https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines).

The page limit of 8 pages includes references.

2. Short Industry Presentations

We invite practitioners to share experiences, challenges, solutions, or emerging ideas that would benefit from academic discussion and collaboration.

Short industry contributions may be submitted in one of the following formats:

a 2-page paper, or up to 5 presentation slides describing the main idea, presenters, and relevant experience.

Submit your contribution via EasyChair:

https://easychair.org/conferences/?conf=xp2026

Target Audience

Researchers and practitioners who study, work in, or lead agile teams supported by AI, including product owners, Scrum Masters, agile coaches, developers, designers, test engineers, and data scientists. We especially welcome socio-technical, organizational, and human-centered perspectives aligned with XP’s people-and-teams focus.

Why Submit?

• Contribute to one of the fastest-growing topics in the XP community
• Engage with leading researchers and practitioners working on human–AI collaboration
• Influence future empirical research and industry practice
• Opportunity for post-proceedings publication

Program Committee

Astri Barbala - SINTEF and LERO, University of Galway

Julian Bass - University of Salford

Marthe Berntzen - Knowit and University of Oslo

Torgeir Dingsøyr - Norwegian University of Science and Technology

Anh Nguyen - University of South Eastern Norway.

Peggy Gregory - University of Glasgow

Eduardo Guerra - Free University of Bozen Bolzano

Jan Henrik Gundelsby - Knowit

Tomas Herda - Austrian Post

Michael Neumann - University of Applied Sciences and Arts Hannover

Maria Paasivaara - LUT University and Aalto University

Victoria Pichler - Austrian Post

Adam Przybylek - Lero, University of Galway

Anastasiia Tkalich - BTH and SINTEF

Organizers

Nils Brede Moe, Chief Scientist, SINTEF Digital

Viktoria Stray, Professor, University of Oslo & Senior Researcher, SINTEF Digital