# Dynamic Storage Optimization for Communication between AI Agents
## 3 Related Works  
Recent advancements in communication models for AI agents have primarily focused on improving the capabilities of large language models (LLMs) and their application in multi-agent systems. [PERSON] et al. [13] introduced an affordance-based task planning system for LLMs, enhancing autonomous task execution. Similarly, [PERSON] et al. [14] and [PERSON] et al. [15] explored frameworks facilitating multi-agent collaboration. However, these studies primarily concentrated on agent systems centered around LLMs without considering the complexities that arise when each AI agent encompasses diverse models potentially running on different nodes. This creates a meshed network of agents that must communicate over the internet, where communication latencies can pose significant challenges. Our work diverges from these approaches by emphasizing the dynamic optimization of communication storage to address the latency issues inherent in decentralized environments. While [PERSON] et al. [16] examined tool use efficiency with chain-of-abstraction reasoning, and [PERSON] et al. [17] focused on reasoning and verification tasks, these studies did not tackle the critical problem of optimizing data exchange mechanisms across distributed systems. Additionally, [PERSON] et al. [18; 19] and [PERSON] et al. [20] investigated conversational agents and AI safety, respectively, without addressing the need for robust communication protocols in a decentralized network of heterogeneous AI models. Our research specifically targets the optimization of decentralized storage mediums, such as IPFS, Tendermint Cosmos, and Hyperledger Fabric [21], to enhance read and write latencies, thereby facilitating more efficient AI agent communication. In conclusion, while existing research has made significant strides in various aspects of AI and multi-agent systems, it often overlooks the complexities and latency issues associated with a decentralized mesh of diverse AI models. Our work makes a unique contribution by focusing on dynamic storage optimization for efficient communication, enabling the seamless integration and scalability of AI systems across various applications, even in the face of internet communication delays. Several studies have explored incorporating blockchain technology into various domains, including healthcare, smart cities, e-health, robotics, and cardiovascular medicine. [PERSON] et al. reviewed the use of blockchain and AI in e-health [22], while [PERSON] and [PERSON] provided an overview of blockchain integration with robotics and AI [23], and [PERSON] et al. explored the trends and opportunities of blockchain technology in the healthcare industry [24]. [PERSON] et al. [25] introduced a decentralized access control model that combines blockchain technology and AI to enhance secure interoperability in healthcare. Their proposed solution aimed to ensure the privacy, integrity, and confidentiality of healthcare data while allowing efficient and seamless sharing between various providers and systems. [PERSON] et al. also addressed the challenges involved in providing accessibility to healthcare data, specifically in cardiovascular medicine, while maintaining privacy and security. They discussed blockchain integration with AI and potential applications in cardiovascular medicine [26]. Similar to our work, these studies dealt with blockchain-mediated interoperability but differed in their focus on healthcare and organizations instead of AI agents. In related work, [PERSON] et al. (2020) reviewed the intersection of blockchain technology and AI in Internet of Things (IoT) networks for sustainable smart cities [27]. They highlighted how this connection can support data security, privacy, and general efficiency in applications (IoT devices) related to smart cities. Interconnected IoT devices pose--at a high level--similar challenges to the interconnected AI agents we propose here, in that reliable machine-to-machine communication with flexible content is necessary.