Agent Layer

Components
AgentLayer is a cutting-edge protocol that facilitates the collaboration of autonomous AI agents through various functional components. These components include:
AgentNetwork: Purpose-built for Decentralized AI Agents, designed with a high-performance Ethereum layer-2 network, modular architecture, and strategic alignment facilitated by the $Agent token.
AgentOS: A zero-code AI Agent development and orchestration framework for seamless agent deployment.
AgentEx (AgentFi & Agent Store): A gateway for discovering and investing in AI Agents.
AgentLink: A set of protocols enabling agents to communicate, collaborate, and share incentives with other agents.
ModelHub: Curates collections of open-source State-of-the-Art Language Models (LLMs) for building agents, including the proprietary TrustLLM.

Technical Architecture
This project follows a modular approach, dividing the technical architecture into three levels of modules: AgentNetwork, AgentOS, and AgentEx. These modules are designed to streamline the implementation process and enhance the functionality of the system.

AgentNetwork Layer
The AgentNetwork is purpose-built for Decentralized AI Agents, designed with a high-performance Ethereum layer-2 network, modular architecture, and strategic alignment facilitated by the $Agent token.

The basic environment serves as the runtime environment for AI Agents, encompassing data storage, a container engine, prompt generation framework, integration with multiple LLM APIs, and engines for interacting with and accessing data on the blockchain. This environment provides the necessary infrastructure for AI Agents to function effectively within the system.

• Container Engine: The container engine provides AI agents with an isolated and independent execution environment, responsible for initiating, terminating, and managing containerized applications. It ensures the seamless operation of these applications across diverse operating systems and hardware setups. Containerization is a pivotal technology in contemporary cloud computing and microservices architecture, facilitating swift deployment and scalable services.
• Base Container: The standardized runtime environment for AI Agents is provided by the base container, which specifies the essential requirements for running AI Agents, such as the operating system, libraries, and dependencies. This standardization ensures compatibility across different platforms and enhances the portability of Agents within the system.
• AgentLayer SDK: The AgentLayer Development Kit (SDK) empowers AI Agents to engage and conduct transactions on blockchain networks. Within the basic environment, a variety of Web3 SDKs tailored for different programming languages are available, enabling Agents' developers to interact with the Agentlink protocol and the Agent AVS seamlessly. This functionality allows Agents to perform Agent call、smart contract calls, handle transactions, and access blockchain data effectively.
• Blockchain Data: Direct access to blockchain data within the environment layer includes transaction history, ledger states, and smart contract states. This access is vital for AI Agents to verify the integrity of transactions and operations effectively. It ensures that Agents can securely interact with and validate data on the blockchain, enhancing transparency and trust within the system.
• Basic Prompt Framework: The component in question provides a standardized set of interfaces or templates for generating and processing AI Agents' prompts. These prompts are crucial for natural language processing, enabling Agents to comprehend and respond to user requests effectively. The prompts typically consist of components like requests, framing context, format specifications, and references to enhance the interaction between users and AI Agents.
• LLM API: The API for Large Language Models (LLMs) integrates various LLM programming interfaces, including TrustLLM, enabling AI Agents to undertake more complex multimodal tasks. Through LLM APIs, Agents can engage in sophisticated tasks that involve interactions with diverse models and services. This integration enhances the capabilities of AI Agents, allowing them to perform a wide range of functions beyond traditional text-based interactions.

https://agentlayer.xyz/whitepaper