Agentic Social Affordance Framework (ASAF): Agent Identity Design as a Collaboration Interface in Multi-Agent Systems
arXiv:2606.09832v1 Announce Type: cross Abstract: As AI systems evolve from single conversational agents to complex multi-agent architectures, a critical design dimension has been overlooked: how the social identity of individual agents shapes human behavior within the collaboration. This paper introduces the Agentic Social Affordance Framework (ASAF), a theoretical framework that extends Social Affordance theory into the context of multi-agent AI systems. We propose that agent identity design functions not merely as a user interface convention, but as a collaboration interface -- structuring
As AI systems rapidly evolve into complex multi-agent architectures, the need to understand and design effective collaboration interfaces between humans and these systems becomes paramount.
Sophisticated readers should care because this framework addresses a critical, often overlooked aspect of AI development: how agent identity impacts human interaction and collaboration in advanced AI systems.
The design of AI agents shifts from purely functional considerations to encompass social identity as a core element of the collaboration interface, fundamentally altering human-AI teamwork paradigms.
- · AI ethicists and psychologists
- · Multi-agent system developers
- · Human-computer interaction researchers
- · Organizations deploying collaborative AI
- · Developers ignoring social identity in AI design
- · Companies with poorly integrated multi-agent systems
- · Traditional isolated software development
Companies will begin to prioritize and invest in agent identity design as a key differentiator for their multi-agent AI systems.
This focus could lead to the emergence of specialized roles and agencies dedicated to crafting and managing the social identities of AI agents.
Over time, the perceived social intelligence and trustworthiness of AI agents could become as important as their functional capabilities, profoundly reshaping user adoption and societal integration.
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Read at arXiv cs.AI