# Core Features ## Below is an overview of the key system features: ### AI-Driven Order Routing * Liqui’s AI engine continuously analyzes liquidity conditions, volatility, and historical flows to determine the most efficient execution path. **How it Works:** * The AI agent collects data from MegaETH liquidity pools * Simulates the best route based on slippage and depth * Sends the decision to smart contracts via the oracle * The contract executes the trade atomically ### Real-Time Liquidity Mapping **The AI continuously monitors the state of liquidity across the network:** * Identifies depth, spread, volatility, and TVL * Detects fragmentation or anomalies * Updates the routing map in near real time **Implementation:** * AI connects via RPC or WebSocket to MegaETH DEX pools * Continuously refreshes the internal map * Sends results or hashed data to on-chain contracts ### Predictive Execution Modeling **Before any trade is executed, the AI simulates potential outcomes:** * Assesses slippage risk and fill probability * Predicts latency and price impact * Models the effect on subsequent orders **Implementation:** * AI is trained on historical data * Submits a risk-assessed payload before execution * Contract verifies slippage conditions before committing ### Secure Smart Contract Execution * Trades can be split and routed across multiple pools * Slippage and gas limits are enforced * Execution is atomic - all or nothing ### AI Feedback Loop After each trade, the AI updates its internal models based on execution results. * Receives logs (fill success, gas used, price deviation) * Refines routing and prediction models * Improves execution efficiency over time --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.liqui.io/core-features.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.