Brief review of the AI Agent framework D.A.T.A released by CARV

D.A.T.A is a data framework for general ‘chain-based’ scenarios, mainly addressing the enhancement of AI Agent data interaction capabilities such as cross-blockchain data processing, privacy computation, and automated decision-making.

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 where they can autonomously plan actions and make decisions. Its main target audience is LLMs large models.

Allowing large 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). HLP formulates strategies and tasks, while 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 decisions for NPCs or players in games.

In contrast, CARV provides the D.A.T.A framework, which is a general-purpose ‘data’ infrastructure aimed at providing high-quality on-chain and off-chain data support for AI Agents. Its main service targets are the inter-chain ‘data’ communication and interaction capabilities of AI Agents.

As a modular and highly scalable general-purpose 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 blockchain’s verifiable and traceable mechanism ensures the security of data during transmission and processing, and the application of TEE and ZK technology ensures privacy. It is easy to see that CARV primarily defines a mechanism 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 for the inter-chain interaction adapted for AI Agent: SVM Chain, D.A.T.A framework, CARV_ID, and 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 necessary supporting chain infrastructure 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 or, and associating it through 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’s identity marking, identity verification, permission management, data authorization, etc., mainly working collaboratively with the D.A.T.A framework system for data management.

4. CARV_Labs provides basic support for the landing of AI Agent applications through project incubation, ecological application landing, and support for technological innovation, etc., so that AI Agent applications supported by other technological framework modules can truly land.

In summary, it is clear that the way CARV enters the AI Agent track is to leverage its inherent advantages of a chain structure, seize the ‘feature point’ of processing on-chain and off-chain data required for the normal operation of AI Agents, aggregate data, define data standards, and establish data verification and traceability mechanisms, thereby making CARV a blockchain architecture that can support AI Agents.

There is a fundamental difference between the G.A.M.E and D.A.T.A frameworks. One focuses on the autonomous decision-making and action execution capabilities of AI agents in game scenarios, allowing AI agents to better understand natural language inputs and translate them into actions within the game. The other spans multiple chain environments and attempts to create a universal infrastructure chain called CARV that serves AI agents by taking a data-driven approach.