[Post] Application of AI Agent Technology in the E-commerce Industry, Stability Assurance, and System Improvement Solutions

Application Models of Web3 and AI Agents in E-commerce

AI agents combine customer data with Web3 ownership information to provide personalized recommendations, and perform real-time dynamic pricing by analyzing the demand and scarcity of NFT·tokenized goods. Additionally, in decentralized markets, smart contracts can be used to automate search·bidding·purchasing, and on-chain records can be used to detect counterfeit·fraudulent transactions to enhance credibility. Personal and enterprise data can be tokenized and traded, with AI learning from these data to build generative models and create a cyclical ecosystem for resale. In the metaverse·AR/VR environments, AI chatbots and virtual assistants can handle natural language responses, recommendations, and even payments, providing immersive shopping experiences.

Technical and Social Issues in E-commerce Based on AI Agents and Web3

AI agents’ black-box nature makes decision basis difficult to verify, and errors in interactions with smart contracts may directly lead to asset loss. Moreover, insufficient compatibility among different chains·data standards can cause interoperability issues, and security·privacy risks such as wallet key theft or prompt injection are also prominent.

In the Web3 environment, verifying trusted data sources is challenging, and counterfeit asset information may be mixed into AI training data. On a societal level, as AI becomes a trading subject, concerns about unclear responsibility attribution may lead to decreased trust and changes in employment structures. Additionally, biased AI judgments and algorithm abuse could amplify market disruptions or manipulation risks such as scams.

Legally, AI’s lack of legal subject status results in ambiguous contract validity and responsibility scope. Consumer protection and personal data regulation are also insufficient. Furthermore, the tax and financial regulatory systems related to digital asset trading are not yet mature, potentially creating regulatory gaps.

Stability Assurance and System Improvement Solutions for AI Agents and Web3 in E-commerce

AI agents lack legal subjectivity; therefore, all ownership and responsibility in e-commerce are attributed to wallet holders. Agents act only as delegated executors, with their permissions and role structures clearly recorded via DID. To ensure interoperability with Web3 environments, standards such as MCP and common metadata models should be introduced, along with DID·VC-based authentication and transaction integrity management mechanisms. Technical safeguards like enhanced smart contract security design, fuzz testing, audits, least privilege principles, input validation, and loss mitigation devices should be implemented to prevent misoperations·fraud·asset loss. Wallet control should also adopt session keys, multi-signature confirmation, role separation, and other schemes. AI models need continuous monitoring for prompt injection, data bias, addiction issues, and should include systematic logging and version control.

On the governance level, an AI management system based on ISO/IEC 42001 should be established, and on-chain policies should incorporate mechanisms such as legal entity verification, delegation, time locks, and auditability to ensure transparency. Mandatory annotation should be enforced to prevent AI agents from being mistaken for humans, and transparency can be improved by openly disclosing the sources and authenticity of recommendation and decision-making basis. Finally, personal information processing should follow the principle of minimal collection, using zero-knowledge proofs and other verification methods to prove only necessary facts, thereby reducing privacy infringement.

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