MuleRun builds a “see-and-calculate” on-chain AI agent: built-in high-quality data + low-cost inference, reshaping transaction efficiency

BlockBeats News, April 21 — The world’s first self-evolving personal AI project MuleRun (Mule Run) Chief Technology Officer Shu Junliang shared at the offline event themed “Decoding Web 4.0: When AI Agents Take Over On-Chain Permissions” that in terms of information acquisition capabilities, traditional data retrieval methods relying on search engines or free APIs are insufficient to meet high-precision demands, especially in financial trading scenarios where free market data interfaces have latency and data loss issues, making it impossible to support high-frequency or professional decision-making.

To this end, MuleRun pre-integrates high-quality data sources and specialized tool interfaces, lowering user access barriers. Users can call upon a data system covering on-chain data, US stock data, and multi-dimensional analysis capabilities without having to purchase or configure APIs themselves.

At the same time, the platform introduces various “Strategy Skills,” including trading strategy evaluation, multi-role simulated debates, and quantitative backtesting. The backtesting tasks are executed on external servers, solving the problem of insufficient computing power in local and general cloud environments, thereby enhancing the practical experience for quantitative users. Additionally, MuleRun is expanding multiple data sources, such as integrating cross-border e-commerce product data, to enhance the Agent’s information acquisition capabilities across different business scenarios.

On the “Intelligent Decision-Making” level, Shu Junliang emphasized that the core of the Agent lies in its ability to handle complex tasks, including multi-cycle data analysis, indicator calculation, and strategy execution. Meanwhile, cost control has become one of the key metrics. Currently, AI systems generally face high Token consumption issues, which directly impact user costs. In response, MuleRun systematically optimizes Token usage at the engineering level, significantly reducing costs while ensuring task completion quality, and improving task execution efficiency under a fixed budget.

Shu Junliang stated that data quality and model efficiency will directly determine the performance of AI Agents. In the future, the platform will continue to seek a balance between high-quality data access and low-cost intelligent computing to support more complex on-chain application scenarios.