Socio-technical congruence is the fit between the coordination required by a project’s technical dependencies and the coordination its participants actually perform. The construct was developed for human software teams, where assignments are stable, timescales are comparable, channels are observable, and dependencies are code-level. Once AI agents join such collectives, these conditions break down. Existing taxonomies map the design space of human-agent collectives, but do not adapt the dependency-coordination construct itself. We contribute a typology of eight incongruence modes as a vocabulary for the structural breakdowns that human-agent collectives produce. We derive the typology by naming four scope conditions the original construct implicitly relied on, redefining the coordination-requirements matrix over a typed and time-varying actor and dependency graph, and enumerating the asymmetric ways the four conditions fail. To give each mode a testable form, we specify a generative model of human-agent collectives and use Monte Carlo simulation to parameterize it, yielding one incongruence rate curve per mode as a starting point for empirical work.
J. Oppenlaender, Aku Visuri, S. Hosio· 0 citations
Collaborative knowledge work is changing in ways that go beyond disclosure or transparency. LLM agents are now embedded in how teams research, design, write, and decide: mediating between members, synthesizing inputs, reformulating ideas, and drafting shared outputs. They do not only facilitate collaboration; they operate within the workflow at the moment contributions are being formed. In doing so, they risk undermining the social conditions under which contributions can be witnessed, attributed, and held accountable. This workshop brings together researchers and practitioners to confront what we call contribution dissolution: the blurring of attribution, originality, and accountability in agent-mediated collaborative work. We argue that this dissolution begins before collaboration itself, in the individual worker's own uncertainty about what is genuinely theirs, and propagates through collaborative relationships, collapsing the reliability that makes productive intellectual exchange possible. Through position statements, mapping exercises, and a hands-on activity, participants will surface how framing accountability as a documentation problem (e.g., AI use statements, watermarking, provenance logs) overlooks the conditions under which accountability is produced. Our goal is to produce a shared research agenda and the foundations of an infrastructural response to contribution dissolution in collaborative knowledge work.
Kashif Imteyaz, M. Rifat, Divya Ramesh et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
