Z4R0
  • Background
    • 🔥The Market Changed. The UX Didn’t.
  • introduction
    • 📑Introducing Z4R0
  • Core Capabilities of Z4R0
    • 💡Modular Agent Framework
    • ❌On-Chain, Cross-Chain Execution
    • ⏲️Real-Time Sensing Layer
    • 🖥️Rule-Based Execution Engine
    • 📳Swarm Mode
    • 🖱️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
    • 🏗️Agent Architecture
    • ⌨️Execution Engine
    • 🎞️Swarm Coordinator
    • ❎Cross-Chain Layer
    • 🔌Plug-in SDK & Developer Interface
  • Why Z4R0 Isn’t Just Another Protocol?
    • ❔Why Z4R0 Isn’t Just Another Protocol?
  • Tokenomics
    • 💰Tokenomics — $ZRX
  • Roadmap
    • 🚀Roadmap
  • FAQ
    • ❓FAQ
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  1. Core Capabilities of Z4R0

Rule-Based Execution Engine

Nothing Executes Without a Rule to Approve It

Z4R0 doesn’t believe in blind automation. Every action an agent takes — no matter how small — must first pass through a system of enforceable rules.

These rules operate in two layers:

  • One defined by the user or strategy module (e.g., “don’t act if gas is too high”)

  • One defined by the protocol itself (e.g,. hard fail-safes, frequency limits)

The engine functions as a live checkpoint. Before any transaction goes through, the agent asks:

  • Is the market still within acceptable risk parameters?

  • Has the agent exceeded any frequency or retry limits?

  • Are resource requirements (gas, liquidity, slippage) still valid?

  • Is there a fallback action if something goes wrong?

If any answer is negative, the execution is paused, delayed, or cancelled outright.

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Last updated 4 days ago

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