Decoding the DATA framework: How to reconstruct the multi-chain interactive ecosystem?

Written by: Haotian

Recently, @carv_official released a set of DATA frameworks and standards. As the name implies, Virtual's GAME is a development and deployment framework focused on game scenarios, while DATA is a data framework for general "chain" scenarios, mainly solving problems such as cross-blockchain data processing, privacy computing, and automated decision-making to enhance AI Agent data interaction capabilities. Below, let's talk about our understanding of DATA in comparison with the GAME framework:

1) The GAME framework provided by @virtuals_io helps developers create AI agents that can autonomously plan actions and make decisions in game scenarios. Its main service object is the LLMs large model.

The large model can make autonomous decisions and action plans based on natural language input through a set of fine-tuned high-level planners (HLP) and low-level planners (LLP). HLP formulates strategies and tasks, while LLP converts tasks into specific executable actions. Ultimately, developers can quickly build and deploy AI Agents that can be used in production environments based on modular components. For example, in games, it can provide intelligent decision-making for NPCs or players.

In contrast, the DATA framework provided by CARV is a "data" infrastructure for general scenarios, whose goal is to provide high-quality on-chain and off-chain data support for AI Agents. Its main service object is the inter-chain "data" communication and interaction capabilities of AI Agents.

As a modular and 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. At the same time, the verifiability and traceability mechanism of the blockchain ensures the security of data during transmission and processing. In addition, the application of TEE and ZK technology ensures privacy. It is not difficult to see that CARV mainly defines a set of mechanisms for AI Agents to adapt and interact between chains.

2) How to do it specifically? The CARV ecosystem is mainly divided into four core components to adapt to the inter-chain interaction of AI Agent: SVM Chain, DATA framework, CARV_ID, CARV_Labs; see the document for reference https://docs.carv.io/data-ai-framework/getting-started/data-framework-plugin-for-eliza)

1. SVM Chain provides the underlying infrastructure of blockchain, including processing cross-chain transactions, supporting smart contract operations, maintaining consensus mechanisms and other basic functions. This is also the supporting chain infrastructure required for the normal operation of the DATA framework;

2. DATA framework and standards, mainly including cross-chain data standardization, data aggregation and analysis processing, privacy computing support, etc. The process includes taking raw data from SVM Chain and associating it through the ID system and Agent identity system, and finally outputting standardized data to the application layer;

3. CARV_ID identity management system, based on the ERC7231 standard, mainly includes AI Agent identity tagging, identity authentication, permission management, data authorization, etc., and mainly works with the DATA framework system for data management;

4. CARV_Labs mainly provides basic support for the application of AI Agent through project incubation, ecological application implementation, support for technological innovation, etc., and ultimately enables the AI Agent application supported by other technical framework modules to be truly implemented.

In summary, it can be clearly seen that CARV's way of entering the AI Agent track is to give full play to the inherent advantages of its chain structure, grasp the "functional point" of on-chain and off-chain data processing required for the normal operation of AI Agent, and aggregate data, define data standards, and build data verification and traceability mechanisms, so that CARV can become a blockchain architecture that can run AI Agent.

There is an essential difference between the GAME and DATA frameworks. One deeply explores the autonomous decision-making and action execution capabilities of AI Agents in game scenarios, allowing AI Agents to more efficiently understand natural language input and convert it into actions in game scenarios. The other spans multiple chain environments, attempting to be guided by the chain needs of AI Agents and take "data" as the entry point, making CARV the first universal infrastructure chain to serve AI Agents.