Technical Architecture
Last updated
Last updated
The WORLD AI Protocol Architecture is a multi-layered, decentralized ecosystem designed to empower Fully Autonomous AI Agents within the WORLD3 platform. Comprising the AI Agent Portal, WORLD AI Protocol, and Blockchain Layer, this architecture ensures scalability, interoperability, and user-centric intelligence.
The AI Agent Portal serves as the user-facing interface, providing creators with intuitive tools to design, deploy, and manage AI Agents. This layer abstracts complex technical processes, enabling seamless interaction with the WORLD AI Protocol while ensuring accessibility and scalability.
Components:
Create & Deploy Agents: A no-code or low-code interface for building and launching Fully Autonomous AI Agents, supporting drag-and-drop workflows and natural language prompts. Built on a reactive front-end framework (e.g., React, Vue.js) with RESTful or GraphQL APIs for real-time agent configuration. Integrates containerized deployment (e.g., Docker, Kubernetes) for scalability.
Example: A creator designs a customer service AI Agent using predefined templates, deploys it on the platform, and monitors its performance via a dashboard.
Chat: Enables real-time interaction between users and AI Agents, facilitating conversational interfaces for task execution and feedback. Leverages WebSocket connections for low-latency communication, integrated with the LLM for natural language processing and response generation.
Example: A user chats with a DeFi Trading Agent to request real-time market analysis, receiving dynamic responses based on on-chain data.
Add Skill Plugins: Allows creators to extend agent functionality by integrating external or custom plugins from the WORLD AI Protocol. Supports plugin discovery via a decentralized registry (e.g., IPFS-based catalog), with secure API integration using gRPC or REST. Example: A creator adds a “Twitter Skill” plugin to an AI Agent, enabling it to retweet content and monitor feeds autonomously.
Add Knowledge Packs: Equips agents with domain-specific expertise through knowledge repositories, enhancing their decision-making capabilities.Utilizes vector databases (e.g., Pinecone, Azure AI Search) for semantic knowledge retrieval, updated via real-time data feeds.This layer ensures intuitive agent creation and management, connecting creators to the WORLD AI Protocol for intelligent execution and external services for broader integration.
Example: A creator adds a “DeFi Knowledge Pack” to an agent, enabling it to provide insights on Uniswap liquidity pools and yield farming strategies.
The WORLD AI Protocol forms the intelligence backbone of WORLD3, providing AI Agents with advanced capabilities, modularity, and adaptability. It orchestrates agent behavior through a combination of core components and sub-task execution, interfacing with external actions and the Blockchain Layer.
Components:
AI Agent:
Knowledge Packs: Domain-specific datasets and expertise (e.g., DeFi trends, social media strategies) stored as vector embeddings for semantic retrieval. Integrated with RAG (Retrieval-Augmented Generation) techniques to combine knowledge with LLM outputs for accurate responses.
Example: An AI Agent uses a “Web3 Knowledge Pack” to generate reports on the latest blockchain events for a user.
Skill Plugins: Extensible modules for specific tasks (e.g., Twitter management, trading automation), enabling agent customization and automation. Implemented as microservices with API endpoints, supporting real-time data processing and plugin chaining for complex workflows.
Example: A “Twitter Skill” plugin allows an AI Agent to retweet posts and read feeds on X, while a “Trading Bot Skill” automates DeFi trades on Uniswap.
Personality: Configurable behavioral traits for agents, ensuring consistent and user-friendly interactions.Uses rule-based or machine learning models to adjust tone, style, and responsiveness based on user preferences.
Example: An AI Agent adopts a friendly, professional tone for customer service interactions.
Memory: Maintains contextual awareness through short-term and long-term storage, enabling continuity in conversations and tasks. Employs distributed memory systems (e.g., Redis, Cassandra) with compression for efficiency, integrated with the LLM for context retention.
Example: An agent remembers a user’s previous DeFi trading preferences to suggest optimized strategies.
Agentic Wallet: Manages on-chain assets and transactions for agents, ensuring secure and decentralized operations. Uses cryptographic key management (e.g., ECDSA) and integrates with Layer 3 smart contracts for asset handling.
Example: An agent uses its Agentic Wallet to execute a trade on CoinGecko, paying with $WAI tokens.
Planning Module: Breaks down complex tasks into sub-tasks, enabling autonomous planning and execution. Utilizes graph-based planning algorithms and reinforcement learning for dynamic task prioritization, integrated with the Execution Module.
Example: An AI Agent plans a multi-step DeFi trading strategy, identifying arbitrage opportunities and executing trades sequentially.
Concurrent Execution Manager: Coordinates multiple agent tasks simultaneously, ensuring efficient resource utilization.Employs distributed computing frameworks (e.g., Apache Kafka) for task scheduling and load balancing across nodes.
Example: An agent manages concurrent tasks, such as posting on X and analyzing CoinGecko data, without performance degradation.
Execution Module: Executes planned tasks, logging activities for transparency and auditability. Integrates with external APIs and smart contracts, using event-driven architectures for real-time action execution.
Example: An agent executes a trade order on CoinGecko, logs the transaction on the blockchain, and submits a Discord message with the results.
Execution Logger: Records agent actions, sub-tasks, and outcomes for analysis, debugging, and compliance. Stores logs in a decentralized ledger (e.g., IPFS, Ethereum) with timestamping for immutability, accessible via API queries.This layer ensures AI Agents are intelligent, adaptable, and capable of executing complex, multi-step workflows, interfacing with external services and the Blockchain Layer for decentralized operations.
Example: Logs show an AI Agent’s successful retweet on X, including timestamps and performance metrics.
The Blockchain Layer underpins the WORLD AI Protocol with decentralization, security, and interoperability, anchoring AI Agents in Web3 technologies. It ensures trust, transparency, and community-driven governance while supporting agent operations and asset management.
Components:
Multichain WORLD AI Protocol Contracts: Smart contracts deployed across multiple blockchain networks (e.g., Ethereum, Polygon) to manage agent operations, asset creation, and governance.Built using Solidity or Rust, with cross-chain compatibility via bridges (e.g., Chainlink CCIP). Includes formal verification for security.
Example: A smart contract automates the deployment of an AI Agent on Ethereum, managing its Agentic Wallet and $WAI transactions.
Decentralization & Governance: Decentralized mechanisms allowing the community to influence platform development and resource allocation.Uses DAOs with on-chain voting powered by $WAI-weighted ballots, implemented via smart contracts with quadratic voting support.
Example: The community votes on-chain to prioritize development of a new Skill Plugin for DeFi trading, using $WAI tokens.
AI-Generated Assets (AGAs): Digital assets created by AI Agents, tokenized as NFTs for ownership and trading.Minted as ERC-721 tokens on the blockchain, with metadata stored on IPFS for scalability and accessibility.This layer ensures secure, transparent, and decentralized operations, enabling AI Agents to interact with on-chain assets, execute smart contract-based tasks, and participate in community governance.
Example: An AI Agent generates a unique DeFi strategy as an AGA, which a user purchases on the Marketplace for $WAI.
The WORLD AI Protocol Architecture integrates with millions of web services and APIs, expanding agent capabilities through external actions. Key integrations include:
X (Twitter): Enables AI Agents to retweet, read feeds, and post updates, leveraging the “Twitter Skill” plugin.Uses Twitter API v2 with OAuth authentication, integrated via Skill Plugins for real-time social media management.
Example: An AI Agent retweets a user’s post on X and logs the action in the Execution Logger.
CoinGecko: Facilitates DeFi trading by checking trade balances and submitting orders, integrated via the “Trading Bot Skill.”Connects to CoinGecko’s REST API for market data, with secure transaction execution via Layer 3 smart contracts.
Example: An agent checks a user’s ETH balance on CoinGecko, executes a trade, and updates the Agentic Wallet.
Discord: Allows agents to submit messages and notifications, enhancing user communication. Integrates with Discord’s Webhook API, secured with token-based authentication and logged via the Execution Logger.
Example: An agent sends a Discord message reporting a successful DeFi trade, including profit details.
On-chain Games: Enables agent interaction with decentralized gaming platforms, such as submitting transactions or playing/interacting with games. Uses Web3.js or Ethers.js for smart contract interactions, with real-time data processing via blockchain nodes.This integration ensures AI Agents can access and interact with a vast ecosystem of web services and APIs, enhancing their utility and adaptability for diverse applications.
Example: An AI Agent submits a transaction to play a decentralized game, logging the action on the blockchain.
