DoubleAgents: Human-Agent Alignment in a Socially Embedded Workflow
📰 ArXiv cs.AI
DoubleAgents is a system for human-agent alignment in socially embedded workflows, integrating coordination agents and dashboards for improved task delegation
Action Steps
- Identify complex, socially embedded tasks that require human-agent alignment
- Develop a coordination agent that maintains state and proposes plans and actions
- Design a dashboard visualizing agent actions and user feedback to facilitate alignment
- Integrate user preferences and feedback into the agent's decision-making process
Who Needs to Know This
AI engineers and researchers on a team can benefit from DoubleAgents to develop more effective human-agent collaboration systems, while product managers can apply its principles to design more user-centric AI-powered workflows
Key Insight
💡 Human-agent alignment is critical for effective task delegation, and implicit user preferences can be addressed through distributed cognition and visual feedback
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🤖 DoubleAgents: Aligning AI with user intent in socially embedded workflows
Key Takeaways
DoubleAgents is a system for human-agent alignment in socially embedded workflows, integrating coordination agents and dashboards for improved task delegation
Full Article
Title: DoubleAgents: Human-Agent Alignment in a Socially Embedded Workflow
Abstract:
arXiv:2509.12626v3 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 visua
Abstract:
arXiv:2509.12626v3 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 visua
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