인간-AI 협업 속 '팀' AI의 조직행동 연구
이 논문은 Agentic AI가 단순한 보조 도구를 넘어 planner, solver, reviewer 등이 모인 collective로서 조직적 워크플로우에 진입하고 있음을 분석합니다. 저자는 Agentic AI가 업무 분화와 조정 등의 측면에서 인간 조직과 유사하지만, motivation이나 trust 대신 context architecture에 의해 유지된다는 차이점을 제시합니다. 또한 contextual transaction cost를 중심으로 human-imitation 방식보다 shared-state 및 adaptive 방식이 더 효율적임을 실험적으로 입증합니다.
arXiv:2606.30986v1 Announce Type: cross Abstract: Agentic artificial intelligence is increasingly deployed not as a single assistant but as a collective of planners, solvers, reviewers, memory managers, tool users, and orchestrators. These systems are entering organisational workflows under familiar labels such as teams, managers, committees, markets, and workflows. This article asks whether such agent collectives exhibit organisational behaviour in a sense that is analytically comparable to, yet distinct from, human organisational behaviour. I argue that agentic AI is a partial organisational analogue. It resembles a human organisation because it differentiates work, coordinates interdependence, performs recurrent routines, crosses boundaries, and produces collective outcomes. It differs because these patterns are not sustained by motivation, identity, trust, employment, socialisation, or moral accountability. They are sustained by context architecture: prompts, memory, traces, schemas, tools, validators, and permissions. The article develops contextual transaction cost as the central mechanism linking these similarities and differences. Computational theorising, synthetic task simulations, real LLM agent traces, and robustness analyses show that human-imitation forms often underperform when they add lossy handoffs, correlated deliberation, and verification burdens, whereas shared-state and adaptive forms perform better when they make context durable, inspectable, and task-contingent. The article contributes to organisation studies by theorising agentic AI as an emerging object of organising and by specifying the interface conditions under which human and agentic organisational behaviour can jointly support collective intelligence.