🤖 AI Summary
Current AI tools exhibit fragmentation in professional domains, supporting only isolated task augmentation without enabling sustained, adaptive human-AI collaboration. To address this, we propose a unified human-AI collaboration architecture centered on *process as the first-class citizen*, jointly modeling interaction, process logic, and infrastructure. This enables explicit, verifiable, and dynamically adaptive process representation—the first such realization. Our novel paradigm—verifiable, intervenable, and evolvable human-AI processes—supports cross-task and longitudinal goal alignment and co-evolution. We overcome tool silos via Process-as-a-Service (PaaS), observable collaborative state design, and multi-granularity intent alignment. The resulting architecture provides a scalable foundation and empirically grounded design principles for collaborative AI platforms. (136 words)
📝 Abstract
As AI tools proliferate across domains, from chatbots and copilots to emerging agents, they increasingly support professional knowledge work. Yet despite their growing capabilities, these systems remain fragmented: they assist with isolated tasks but lack the architectural scaffolding for sustained, adaptive collaboration. We propose a layered framework for human-agent systems that integrates three interdependent dimensions: interaction, process, and infrastructure. Crucially, our architecture elevates process to a primary focus by making it explicit, inspectable, and adaptable, enabling humans and agents to align with evolving goals and coordinate over time. This model clarifies limitations of current tools, unifies emerging system design approaches, and reveals new opportunities for researchers and AI system builders. By grounding intelligent behavior in structured collaboration, we reimagine human-agent collaboration not as task-specific augmentation, but as a form of coherent and aligned system for real-world work.