DoubleAgents: Human-Agent Alignment in a Socially Embedded Workflow

Abstract:Aligning agentic AI with user intent is critical for delegating complex, socially embedded tasks, yet user preferences are often implicit, evolving, and difficult to specify upfront. We present DoubleAgents, a system for human-agent alignment in coordination tasks, grounded in distributed cognition. DoubleAgents integrates three components: (1) a coordination agent that maintains state and proposes plans and actions, (2) a dashboard visualization that makes the agent's reasoning legible for user evaluation, and (3) a policy module that transforms user edits into reusable alignment artifacts, including coordination policies, email templates, and stop hooks, which improve system behavior over time. We evaluate DoubleAgents through a two-day in-lab interactive simulation study (n=10), three real-world deployments, and a technical evaluation. Participants' comfort in offloading tasks and reliance on DoubleAgents both increased over time, correlating with the three distributed cognition components. Participants still required control at points of uncertainty - edge-case flagging and context-dependent actions. We contribute a distributed cognition approach to human-agent alignment in socially embedded tasks. We further introduce interactive simulation as a methodological testbed for rapid iteration and alignment testing of agentic systems.

22 pages, 6 figures. ACM Conference on Human-AI Complementarity and Alignment (HCOMP 2026)

Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Emerging Technologies (cs.ET)

arXiv:2509.12626 [cs.HC]

(or arXiv:2509.12626v4 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2509.12626 Focus to learn more arXiv-issued DOI via DataCite

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