AULONG has announced the completion of a $5 million Series A funding round, jointly led by Khosla Ventures and Symbolic Capital. The proceeds will be primarily allocated to financial data set training, compute token acquisition, AULONG OS technology iterations, and the development of a decentralized compute orchestration network, accelerating AULONG’s evolution from an intelligent trading entry point into a global financial AI agent infrastructure.

For AULONG, this funding round represents not only capital support but also a critical milestone in its transformation from a “financial intelligent agent product” to a “financial Agent OS.” As AI agents move from conversational interaction into task execution, market expectations are rapidly shifting. Users no longer seek tools that only generate analytical reports or trading suggestions; instead, they require systems capable of understanding objectives, invoking tools, identifying risks, recording processes, conducting post-execution reviews, and continuously evolving through feedback loops.

AULONG’s entry point into financial markets is driven by the sector’s high-frequency data, complex variables, well-defined risk boundaries, and measurable feedback, making it a high-intensity environment for training execution-oriented AI agents. By leveraging finance as an entry point, AULONG aims to build an intelligent agent operating system spanning data, strategies, skills, risk control, auditing, and settlement, enabling AI agents to move beyond decision support into full-cycle task execution.

AULONG stated that the joint lead investment from Khosla Ventures and Symbolic Capital represents a strong validation of its strategic direction from the capital market. “Our goal is not to scale a trading bot, but to build the infrastructure for financial AI agents. Trading is only the entry point. The real objective is to connect data, strategies, skills, risk control, auditing, and settlement into a continuously evolving Agent OS.”

The funding will focus on four key areas: first, building integrated training datasets across financial, on-chain, and user behavioral data; second, integrating compute tokens and GPU resource pools to enhance the training and inference capabilities of AULONG Brain; third, advancing iterations of AULONG OS core modules, including Memory, Review, Skill Router, and Agent Engine; and fourth, exploring decentralized compute orchestration to create a closed loop between model services, skill invocation, and on-chain settlement.

Regarding the investment, Vinod Khosla founder of Khosla Ventures stated: “AULONG’s value lies not only in trading tools themselves, but in its integration of financial data processing, intelligent agent execution systems, and AI workflow automation. Financial markets are a high-intensity environment for training AI agents. If a system can establish stable data loops, strategy validation, and risk execution in such an environment, it has the potential to evolve from a trading entry point into a broader enterprise AI OS.”

AULONG believes this perspective represents a key industry signal from the current funding round. Over the past year, many AI trading tools and automated investment products have emerged, but most remain at the stage of generating suggestions. They can interpret market movements but struggle to form closed execution loops; they can output strategies but lack long-term review mechanisms; and they can integrate tools but often lack clear permissioning, auditing, and trusted settlement frameworks. AULONG aims to address precisely this gap.

The participation of Symbolic Capital will further strengthen AULONG’s development in trusted on-chain infrastructure. AULONG noted that Symbolic Capital’s focus centers on the Agent identity, task credentials, audit trails, and Agent-to-Agent settlement capabilities supported by AULONG Chain.

Following the completion of this funding round, AULONG will prioritize the development of its financial data training system, core AULONG OS iterations, AULONG Chain task auditing capabilities, and decentralized compute orchestration network. It will also expand ecosystem collaborations around developer skills, enterprise APIs, risk control systems, on-chain task auditing, and decentralized compute infrastructure.

AULONG stated that while the project will remain anchored in financial markets, it will not be limited to them. The execution, risk control, and review capabilities developed in financial environments will serve as the foundation for broader expansion into the AI agent service ecosystem. With this funding round, AULONG’s evolution from an intelligent trading product to financial AI agent infrastructure is gaining further recognition from international investors.