Building Persona-Based Agents On Demand: Tailoring Multi-Agent Workflows to User Needs
For developers and users of agentic AI platforms, this work addresses the limitation of fixed agent configurations by enabling real-time persona adaptation, though it is a conceptual proposal without empirical validation.
The paper argues that current multi-agent systems rely on hard-coded architectures that limit personalization, and proposes a pipeline for on-demand persona-based agent generation to dynamically tailor agents to user needs and task contexts.
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.