Core Components

The Intelligence Layer of DAPPOS forms the cognitive backbone of the Web3 AI Operating System, orchestrating research, planning, and decision-making for crypto-related tasks. It is structured of two core components: the Multi-Agent Framework (MAF) and the Bubble Engine, which together enable scalable, adaptive intelligence tailored to Web3's dynamic environment.

The Multi-Agent Framework (MAF) serves as the orchestration module, comprising 300–400 specialized vertical agents and over 200 integrated tools. Built on a stateful graph backbone, MAF facilitates composable autonomy through deterministic routing, subgraphs for tasks, and advanced features such as multi-agent search and tool-augmented generation. This ensures explainable, production-grade AI that excels in domain-specific reasoning and generalization across quantitative analysis, trading strategies, and market insights.

Complementing MAF, the Bubble Engine acts as the adaptive learning core, powered by continuous reinforcement learning (RL) models optimized for Web3. It perpetually evolves by ingesting real-time data from sources like X and Binance Square, incorporating user-contributed insights via the Bubble task platform. Key mechanisms include Contextual Retrieval-Augmented Generation (RAG) for provenance-first data aggregation and Compound Memory for blending durable knowledge with episodic learnings, while handling misinformation and leveraging Web3 incentives for collaborative growth.

Together, MAF and the Bubble Engine help DAPPOS create a synergistic system that outperforms generic AI in Web3, delivering excellent and evolving intelligence ready for the Execution Layer.