Ecosystem Component Interaction
Overview
This document outlines the complete architecture for how Liqui leverages off-chain AI agents and oracles to dynamically interact with on-chain smart contracts - both in the browser-based DEX and future desktop deployments.
1. Architecture Summary
AI Agents (Off-chain)
Monitor, analyze, and forecast market conditions, risk, liquidity, and volatility; Execute trades, adjust parameters, and automate liquidity management
Oracle Infrastructure
Securely transmits AI-generated insights to smart contracts
Smart Contracts (on-chain)
Role not specified in the original text, but typically would handle on-chain logic, asset management, and execution based on oracle inputs.
Web App (Browser UI)
User-facing interface for trading, portfolio, and decision tools
Desktop App (Planned)
High-performance local AI/UX client with direct backend integrations
2. Data Flow: Off-chain AI - Oracle - Smart Contract
Step-by-Step Workflow
AI Agent collects and processes data:
Pulls real-time feeds from MegaETH, DEX trades, oracles, and macro market APIs
Runs ML/LLM models to predict volatility, optimize liquidity routes, or suggest margin recalibration
Data sent to Oracle Layer:
AI results are aggregated, signed, and submitted to the oracle interface
Oracles verify, timestamp, and relay the data to the relevant smart contract
Smart Contract reacts:
Adjusts limit ranges, executes rebalance logic, adapts fee parameters, or routes orders accordingly
This process is fully autonomous and driven by off-chain intelligence
Feedback Loop:
Smart contract emits events (e.g., slippage, gas usage, execution quality)
AI agent parses the on-chain event logs and feeds them into model training
3. Frontend Support
Browser (Web3 Interface)
UI triggers AI requests via REST or GraphQL API to backend AI engine
Results shown as:
Trade suggestions
Estimated price impact
Risk rating per asset
Users interact via wallets (e.g., MetaMask), signing and sending transactions manually
Desktop (Future Liqui Pro App)
Local Python/LLM module + embedded signer (or wallet integration)
Near-real-time predictions rendered locally
Useful for power users (market makers, institutions)
Can batch-sign, auto-trade via preset AI triggers
4. Security & Trust
AI agents are verifiable, signed, and optionally open-sourced
Oracle layer uses proofs and aggregation to prevent spoofed signals
All smart contracts are public and audit-ready
Feedback from execution is used to score AI agent performance
5. Why This Matters for Liqui
AI-assisted trading gives users a competitive edge
Browser access ensures broad adoption, while desktop tooling supports pro-level operations
The system turns Liqui into a self-adaptive, intelligence-driven DEX - far beyond just AMMs or
CLOBs
6. Example Use Case
A user opens Liqui in browser
AI agent suggests reducing leverage due to rising volatility
Oracle transmits the insight to Liqui's risk manager contract
Contract lowers max leverage by 15% for all users in volatile pair
Users see new limits immediately in UI
Event log is saved, and AI retrains to improve margin calibration logic
Conclusion
Liqui's integration of off-chain AI agents, oracle systems, and on-chain smart contracts enables an entirely new category of decentralized exchange - one that is autonomous, adaptive, and data-driven.
We are not just building a DEX.
We are building the AI-native financial layer of tomorrow.
Last updated