From prototype to persona: AI agents for decision support and cognitive extension

Despite advances in generative AI, most systems prioritize transactional responses over reflective reasoning. This paper presents a human-centered framework for AI persona design aimed at extending cognition, surfacing internal conflict, and aligning digital agents with user values. Originating from early experiments in emotional tone modeling and agentic voice differentiation, the project evolved into two complementary architectures: the AI Cabinet Method, which simulates deliberative, multi-perspective debate through ensembles of purpose-built personas; and DigitalEgo, a modular digital co-pilot aligned to individual user values and tone. In contrast to most generative AI interfaces, this framework emphasizes structured trait modeling, value-driven alignment, and guided interaction protocols to enable context-aware decision support. Through a pipeline combining qualitative intake workflows with modular persona encoding, the framework enables decision support, creative ideation, leadership reflection, and organizational alignment. Simulated friction among personas supports structured evaluation of blind spots, prioritization trade-offs, and perspective alignment. Methodologically, this work blends insights from human-computer interaction, cognitive extension theory, and AI ethics, anchored in a design philosophy that favors augmentation over automation. Initial applications span decision support, narrative testing, and leadership reflection. By embedding dissent, memory, and value structures, this approach enables more intentional, ethically grounded AI interactions. Future research will focus on empirical validation and adaptive tuning at scale.