3.1 Proposal Processing & AI Filtering
As the AI Snapshot, Quack AI establishes the first AI-driven governance execution framework, ensuring that proposals are not just evaluated but also autonomously enforced without manual intervention.
Governance within Quack AI follows a structured, AI-optimized decision-making process that ensures:
Fair and Data-Driven Proposal Evaluations -AI Agents analyze governance proposals using on-chain data, sentiment analysis, and past governance trends to assess their relevance and impact.
Automated Ranking & Prioritization - AI models assign priority levels to proposals based on ecosystem needs, community sentiment, and governance history.
AI-Orchestrated Intelligent Delegation - AI enables automated voting execution, reducing governance bottlenecks and ensuring wider participation from token holders.
Governance proposal submission follows a standardized process, where AI Agents review, analyze, and categorize proposals before they enter the voting phase. This ensures that governance remains focused on meaningful initiatives rather than spam or low-quality proposals.
AI-Driven Proposal Processing Workflow
Proposal Submission: Users submit governance proposals through the AI Governance Hub.
AI Initial Screening: AI Agents evaluate proposals based on historical data, governance priorities, and feasibility factors.
Sentiment & Relevance Analysis: AI models scan community discussions, past governance trends, and market conditions to determine proposal significance.
Automated Ranking & Categorization: AI assigns a governance priority score, ensuring critical proposals receive higher visibility.
Final Review & AI Optimization: Before entering voting, AI refines proposals for clarity, accuracy, and feasibility, ensuring that only viable and impactful proposals progress.
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