Deciphering the D.A.T.A framework: How to reconstruct the multi-chain interactive ecosystem?

Written by Haotian

Recently, @carv_official published a set of D.A.T.A frameworks and standards. As the name suggests, Virtual's G.A.M.E is a development and deployment framework focusing on game scenarios, while D.A.T.A is a data framework for general "chain" scenarios, mainly solving the problem of enhancing the data interaction capabilities of AI agents such as cross-blockchain data processing, privacy computing, and automated decision-making. Let's talk about the understanding of D.A.T.A in comparison to the G.A.M.E framework:

1. The G.A.M.E framework provided by @virtuals_io is an AI Agent that helps developers create game scenarios with autonomous planning and decision-making capabilities. Its main target audience is LLMs large models.

Allowing large-scale models to make autonomous decisions and action plans based on natural language input, through a set of fine-tuned High-Level Planner (HLP) and Low-Level Planner (LLP). The HLP devises strategies and tasks, while the LLP translates tasks into specific executable actions. Ultimately, developers can quickly build and deploy AI Agents for production environments based on modular components. For example, providing intelligent decision-making for NPCs or players in games.

By contrast, CARV's D.A.T.A. framework is a general-purpose 'data' infrastructure aimed at providing high-quality on-chain and off-chain data support for AI Agents. Its primary service targets the inter-chain 'data' communication and interaction capabilities of AI Agents.

As a modular, highly scalable general public chain, its SVM Chain introduces a cross-chain data standardization protocol, enabling AI Agents to uniformly access and process data from different blockchains, while the blockchain's verifiable and traceable mechanisms ensure the security of data transmission and processing. In addition, the application of TEE and ZK technologies ensures privacy. It is not difficult to see that CARV mainly defines a mechanism for AI Agents to adapt and interact across chains.

2. How to do it specifically? The CARV ecosystem is mainly divided into four core components for the inter-chain interaction of the AI Agent: SVM Chain, D.A.T.A framework, CARV_ID, CARV_Labs; see the documentation for reference.

1. SVM Chain provides the underlying infrastructure of the blockchain, including processing cross-chain transactions, supporting the operation of smart contracts, maintaining consensus mechanisms, and other basic functions, which are also the supporting chain infrastructure required for the normal operation of the D.A.T.A framework.

2. D.A.T.A framework and standards mainly include cross-chain data standardization, data aggregation and parsing, privacy computing support, etc. The process includes obtaining raw data from SVM Chain, associating it through the ID system and Agent identity system, and ultimately outputting standardized data to the application layer.

3. CARV_ID identity management system, based on the ERC7231 standard, mainly includes AI Agent's identity tagging, authentication, permission management, data authorization, etc., and mainly works with the D.A.T.A framework system for data management;

4. CARV_Labs mainly provides basic support for the landing of AI Agent applications through project incubation, ecological application landing, support for technological innovation, etc., ultimately enabling AI Agent applications supported by other technological framework modules to truly land.

In conclusion, it can be clearly seen that CARV's approach to entering the AI Agent track is to leverage its inherent advantage of a chain structure, seize the "function point" of processing on-chain and off-chain data required for the normal operation of AI Agents, aggregate data, define data standards, build data verification and traceability mechanisms, thereby making CARV a blockchain architecture that can run AI Agents.

The G.A.M.E and D.A.T.A frameworks have fundamentally different natures. One deeply explores the autonomous decision-making and action execution capabilities of AI Agents in the gaming scene, enabling AI Agents to more efficiently understand natural language input and transform it into actions within the game scene. The other spans multiple chain environments, attempting to meet the chain-oriented needs of AI Agents, taking 'data' as the entry point, and making CARV a universal infrastructure chain that serves AI Agents first.