# AI Agents Meet Blockchain: A Survey on Secure and Scalable Collaboration for Multi-Agents
## 7 Application and Use Case Scenarios
### Asset Management
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Contribution** & **Strength** & **Weakness** \\ \hline \multirow{4}{*}{[24]} & Employs a multi-layered ontology approach to improve communication between AI agents in decentralized context & (1) Reduces data access overhead (2) minimizes unnecessary storage; (3) flexible, mobile, and efficient data processing & (1) Experiment on small-scale simulations only, (2) lack of interoperability \\ \hline \multirow{4}{*}{[82]} & Connects the intangible concepts related to transactions of assets to the concrete elements composing the system & (1) Ensures system regulation, (2) provides reliable recording of transactions, (3) can represent asset-related notions & (1) Slow, (2) cost expensive, (3) requires further considerations about asset notions \\ \hline \multirow{4}{*}{[83]} & Automates the storage and retrieval of multiple related editions while ensuring that contributions by multiple authors are recorded & (1) Feasible, (2) decentralized, and flexible, (3) customizable & (1) Unsuitability of Ethereum Testnet, (2) potential issue with IPFS, (3) need for alternate storage options \\ \hline \multirow{4}{*}{[73]} & Broadens the applicability of LLMs in cryptocurrency trading and establishes a benchmark for trading strategies & (1) Performs well under different market conditions, (2) successful trend predictions & (1) Limited dataset, (2) inappropriate trading frequency, (3) lack of fine-tuning \\ \hline \multirow{4}{*}{[84]} & Addresses incentive alignment and transparency challenges in traditional energy trading mechanisms & (1) Enhances energy management, (2) ensures trades are secure and transparent & (1) High computational overhead, (2) experiment on a small scale only \\ \hline \multirow{4}{*}{[81]} & Leveraging blockchain, DAOs, and LLMs to create financially self-sustained and self-managed digital building & (1) Transparent decision making, (2) real-time visualization, (3) autonomously adjusting smart appliances & (1) Potential financial instability, (2) limited implementation due to the reliance on smart appliances \\ \hline \multirow{4}{*}{[85]} & Provides a powerful tool for modeling and studying the strategic interactions within DeFi governance & (1) Gives an explicit representation of agents’ interactions, (2) enables analysis and predictions of outcomes & (1) The analysis is only limited to two agents, (2) lack of real-world applicability validation \\ \hline \multirow{4}{*}{[81]} & Enables decentralized autonomous collaboration between LLM agents & (1) Allows agents to register themselves, discover the capabilities of others, and assign tasks, (2) practically feasible, (3) allows humans and agents to interact in natural language & Poor performance on zero-shot prompting \\ \hline \multirow{4}{*}{[72]} & Improves alert incident resolution in complex micro-services through a blend of multi-agent, LLMs, and blockchain voting & (1) Effectively detects root causes, (2) increases system reliability and operational efficiency & Increases computation overhead \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of application and use case scenarios.