Z4R0
  • Background
    • πŸ”₯The Market Changed. The UX Didn’t.
  • introduction
    • πŸ“‘Introducing Z4R0
  • Core Capabilities of Z4R0
    • πŸ’‘Modular Agent Framework
    • ❌On-Chain, Cross-Chain Execution
    • ⏲️Real-Time Sensing Layer
    • πŸ–₯️Rule-Based Execution Engine
    • πŸ“³Swarm Mode
    • πŸ–±οΈDAO-Tunable System
  • How Z4R0 Operates in Real Markets?
    • Use Case 1: Capital Rotation Across Liquidity Pools
    • Use Case 2: Anticipating Market Shifts Through On-Chain Sentiment Tracking
    • Use Case 3: Multi-Chain Arbitrage Execution
    • Use Case 4: Staking Strategy Optimization
    • Use Case 5: DAO-Governed Strategy Pivots
  • The Architecture That Powers Z4R0
    • πŸ—οΈAgent Architecture
    • ⌨️Execution Engine
    • 🎞️Swarm Coordinator
    • ❎Cross-Chain Layer
    • πŸ”ŒPlug-in SDK & Developer Interface
  • Why Z4R0 Isn’t Just Another Protocol?
    • ❔Why Z4R0 Isn’t Just Another Protocol?
  • Tokenomics
    • πŸ’°Tokenomics β€” $ZRX
  • Roadmap
    • πŸš€Roadmap
  • FAQ
    • ❓FAQ
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  1. The Architecture That Powers Z4R0

Swarm Coordinator

Distributed Coordination Without Off-Chain Logic

Z4R0’s Swarm Coordinator enables multiple agents to collaborate on complex or distributed tasks through a fully on-chain scheduling and orchestration system.

When a strategy requires parallel execution, such as monitoring different chains, scanning large token sets, or executing synchronized actions, a designated smart contract called the Swarm Coordinator assigns subtasks to available agents. Each agent receives a scoped role, an expected output, and a reporting condition.

All coordination β€” from role allocation to result aggregation β€” occurs through on-chain messages. There is no off-chain dispatcher, no external sync server, and no reliance on middleware. Agent responses are registered on-chain and aggregated deterministically. If quorum or validation conditions aren’t met, execution is halted or rerouted.

The Swarm Coordinator is modular and strategy-aware, allowing developers to customize how agents divide and rejoin task workflows β€” whether they follow a round-robin structure, a quorum-based merge, or a fallback-driven retry chain.

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Last updated 4 days ago

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