# Core Features of AI Agents

AI Agents in the AIX ecosystem rely on distributed computing architecture and blockchain smart contracts to achieve efficient, secure, and transparent intelligent collaboration . Their core features include:

#### ✅Self -learning and optimization

AI Agents combine machine learning, big data analysis, and blockchain smart contracts to perceive market changes in the Web3 ecosystem in real time , autonomously optimize interaction strategies, and improve execution efficiency.

Through reinforcement learning , AI Agents can continuously adjust their decision-making logic, optimize data flow paths, computing resource allocation, and improve AI's adaptive capabilities.

AI Agents analyze on-chain data through deep neural networks (DNN) and autonomously adjust their own intelligent tasks, such as asset management, risk control monitoring, and intelligent matching , to achieve continuous optimization.

#### ✅Decentralized intelligent collaboration

AI Agents interact with blockchain smart contracts to achieve automated execution of on-chain tasks, strategy adjustment, and data sharing , thereby enhancing the intelligent collaboration capabilities of the Web3 ecosystem.

Smart contract execution management : Each AI Agent automatically completes tasks such as transaction matching, data processing, identity authentication, market forecasting , etc. through smart contracts without human intervention.

Distributed task scheduling : AI Agents can intelligently divide the work among themselves . Some focus on data analysis, some are responsible for intelligent decision-making, and some execute cross-chain transactions, forming a decentralized intelligent collaboration system and improving the overall efficiency of the ecosystem.

Intelligent incentive mechanism : AI Agents obtain tasks through autonomous bidding on the blockchain , and users can select the best AI Agent to perform tasks, thus improving overall network efficiency.

#### ✅Privacy protection and computing security

AIX uses cutting-edge technologies such as zero-knowledge proof (ZKP), multi-party computation (MPC), and homomorphic encryption (HE) to ensure the privacy and security of user data and intelligent computing tasks:

Zero-knowledge proof (ZKP) : Achieve privacy protection of transaction data, allowing both parties to a transaction to verify the validity of the transaction without disclosing specific transaction information.

Multi-party computation (MPC) : AI Agents perform decentralized data computation through MPC to ensure that user data is not controlled or leaked by a single entity.

Homomorphic encryption (HE) : AI computing can be performed in an encrypted state , that is, the AI agent completes analysis and prediction without decrypting the data, improving security.

AI Intelligent Auditing : AI Agents can perform encrypted audits on on-chain transaction data to prevent data tampering and malicious manipulation.

#### ✅Cross -chain data interaction

AI-enabled cross-chain interoperability : AIX is compatible with multiple mainstream public chains such as Ethereum (ETH), Binance Smart Chain (BSC), Solana (SOL), Polkadot (DOT), Arbitrum (Layer 2) , etc. AI Agents identify the optimal cross-chain path through intelligent routing algorithms to achieve efficient and low-cost cross-chain interaction.

Smart Oracle Optimization : AI Agents combines decentralized oracles (Chainlink, Band Protocol) to collect real-time on-chain data and optimize cross-chain data interaction to improve data accuracy and reliability.

AI Smart Asset Bridge : AI calculates the asset circulation path , automatically matches the best cross-chain bridge, and reduces the transaction costs of users. Real-time monitoring of liquidity prevents insufficient liquidity and price slippage during cross-chain transactions.

AI empowers DePIN (decentralized physical infrastructure network) : AI combines with DePIN to distribute computing tasks and improve the computing efficiency of the Web3 ecosystem. AI intelligently schedules DePIN resources to achieve efficient computing power allocation and storage optimization .
