B² Network Introduces Agentic AI To Enable Modular AI Agent Collaboration
In Brief
B² Network introduced Signal-Driven Agentic AI, a blockchain-native infrastructure protocol designed to facilitate AI agent collaboration by enabling modular cooperation, autonomous task execution, asynchronous interaction, and seamless on-chain integration.
Layer 2 rollup solution built on Bitcoin, B² Network introduced Signal-Driven Agentic AI, a blockchain-native infrastructure protocol aimed at facilitating collaboration among AI agents. This framework supports modular cooperation, autonomous task execution, asynchronous interaction, and seamless on-chain integration.
The system functions similarly to a coordinated team where responsibilities are distributed, actions are performed independently, and all activities are transparently recorded on-chain, enabling adaptive support and progressive alignment with user needs.
The framework divides an AI agent’s operational process into five functional stages, with each phase managed by a designated specialist agent. The first stage, referred to as “Sense,” involves interpreting both on-chain data and external inputs. The second stage, “Plan,” uses a large language model to convert user intent into a structured sequence of tasks. The “Decide” phase then selects the appropriate tools or models required to accomplish the task. Following this, the “Act” stage carries out the necessary actions, such as executing API calls or writing data to the blockchain. The final stage, “Learn,” assesses outcomes and adapts future responses based on performance.
These agents coordinate through a unified communication standard called the Signal protocol, which facilitates triggering, execution, and auditing of tasks. The B² Rollup and B² Hub serve as submission and aggregation layers, while Bitcoin functions as the settlement and verification layer, ensuring all activity is anchored securely.
This modular infrastructure allows AI agents to operate as interoperable components — each fully on-chain, verifiable, traceable, and compatible across different platforms.
This Signal-Driven Agentic AI architecture addresses three primary limitations in current agent frameworks. It improves interoperability by allowing previously isolated systems to connect via a standardized protocol, functioning similarly to universal hardware interfaces. It also enhances transparency by emitting signed on-chain records for every action, providing clear accountability. Additionally, it supports advanced task management through multi-agent routing, enabling dynamic assignment to the most suitable specialists while maintaining the capacity for system evolution.
AI Agent System Empowers Users To Earn On-Chain Rewards, Own Their Data, And Engage In Transparent AI Networks
For end users, the system provides several benefits. Running AI agents allows users to earn rewards by completing tasks and computations, receiving tokens directly on-chain—functioning similarly to mining, but powered by AI. Personal data is encrypted by default and can only be accessed following a payment, enabling true data ownership and privacy protection. Every action performed by the agents is logged and verifiable, designed to meet standards of compliance and security. AI processes are transparent, with each Signal containing logs, incentive structures, and feedback, allowing users to contribute strategies, suggest improvements, and actively participate in the AI ecosystem.
Although the system may appear similar to MCP, the intended scope is broader. Both Signal-Driven Agentic AI and MCP aim to transition LLM-based intelligence from isolated performance to coordinated networks of agents—modular, composable, and interoperable across decentralized platforms. However, their focus differs: MCP acts as a context middleware suited to lightweight, single-agent applications, while DSN-AI serves as a verifiable communication infrastructure capable of linking thousands of agents.
Emphasis is also placed on native security and integrated economic logic. In this framework, a Signal represents more than just a message—it is a verifiable on-chain event including signatures, timestamps, and Merkle proofs. It supports paid subscriptions, encrypted payloads, and ZK-based access control, aligning it with Web3-native design principles.
The relationship between MCP and DSN-AI is not competitive but complementary. MCP functions as the memory and contextual layer for agents, while DSN-AI serves as the coordination and execution layer across platforms. Developers can build agents with MCP and deploy multi-agent workflows through DSN-AI, together shaping the architecture of next-generation intelligent networks.
B² Network is a Layer 2 rollup solution built on Bitcoin, aiming to bring scalability, smart contract functionality, and Ethereum-like programmability to the Bitcoin ecosystem. The platform recently conducted its Token Generation Event (TGE) on the cryptocurrency exchange Binance.
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About The Author
Alisa, a dedicated journalist at the MPost, specializes in cryptocurrency, zero-knowledge proofs, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
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Alisa, a dedicated journalist at the MPost, specializes in cryptocurrency, zero-knowledge proofs, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
