DoubleAgents: Human-Agent Alignment in a Socially Embedded Workflow
Quick summary
arXiv:2509.12626v4 Announce Type: replace-cross 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
Key takeaways
- arXiv:2509.12626v4 Announce Type: replace-cross 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
Why it matters
The significance is not only the legal text but how it changes product design. Decisions around “DoubleAgents: Human-Agent Alignment in a Socially Embedded Workflow” may reshape data collection, model training, output accountability and market access.
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