The article is devoted to the analysis of fundamental challenges associated with the progressive erosion of trust and the weakening of collective agency in digital communities, and to the formation of an integrated paradigm for designing AI-native platforms aimed at overcoming these effects. The relevance of the study is determined by the paradoxical configuration of the current technological landscape: while generative artificial intelligence (GenAI) is being rapidly deployed in the corporate sector (71% of companies report the use of corresponding solutions by mid-2024), there is simultaneously a high level of anxiety and concern among users (82% in 2025), which limits scaling opportunities, hinders the formation of sustainable practices of joint action, and undermines the accumulation of social capital. The aim of the work is to develop a conceptual Architecture of Hybrid AI-Based Community Governance (HCA-Architecture), capable of institutionalizing structural trust and expanding collective agency through the redistribution of roles between human participants and AI agents. The methodological basis of the study is an interdisciplinary synthesis that combines a systematic literature review in leading scientific databases (Scopus, WoS, ACM, IEEE) with a comparative analysis of empirical data on decentralized forms of governance (DAO) and practices of human–algorithm interaction. Within the proposed approach, a model of the AI-Native Community Wheel (AICF) is constructed, which provides a framework for describing and calibrating key mechanisms of coordination, attention allocation, and infrastructural trust. In the final part of the work, it is demonstrated that the proposed framework makes it possible to recode the algorithmic incentives of digital platforms: from a logic of maximizing attention retention and monetization to a logic of maximizing collective coordination, reliability of interactions, and the reproduction of trust, which forms a necessary condition for the sustainable development of digital public spheres. The presented results and the developed architecture are intended for application in research in the field of Human–Computer Interaction, in the design and development of Web3 platforms, in practices of algorithmic governance, and in the architecting of DAO systems, where formalized mechanisms for maintaining trust and distributed agency are required under conditions of high algorithmic mediation.