π€ AI Summary
This work proposes a persona-driven, on-demand multi-agent pipeline to overcome the limitations of conventional agent systems that rely on fixed roles and static interaction protocols, which struggle to accommodate usersβ personalized needs and dynamic task contexts. By integrating real-time persona modeling, context-awareness, and multi-agent coordination mechanisms, the framework dynamically constructs adaptive collaborative systems at runtime. This approach transcends the constraints of traditional static architectures by enabling just-in-time generation and integration of agent personas, thereby significantly enhancing the systemβs adaptability to user characteristics and task-specific contexts, as well as its interactive efficiency. The proposed paradigm offers a foundation for next-generation, highly adaptive multi-agent platforms.
π Abstract
Recent advances in agentic AI are shifting automation from discrete tools to proactive multi-agent systems that coordinate multi-specialized capabilities behind unified interfaces. However, today's agent systems typically rely on hard-coded agent architectures with fixed roles, coordination patterns, and interaction flows that limit end-user personalization and make adaptation to individual needs and contexts difficult. Given this limitation, we argue that on-demand persona-based agent generation offers a promising path towards more efficient and contextually appropriate interaction within agentic workflows. By dynamically crafting agents and personas at run-time to match user characteristics, task demands, and workflow context, agentic platforms can move beyond one-size-fits-all configurations. We present a pipeline for on-demand persona generation in agentic platforms, detailing how real-time crafting of AI personas can be systematically integrated within agent systems, aiming to open new possibilities in agentic platform design paradigms.