It is designed to reflect the operational structure of a quantitative hedge fund, mapping complex control problems onto a hierarchy inspired by fund employee architecture.
The system operates as a layered architecture:
Focus on specialized tasks, such as analyzing specific market conditions, identifying opportunities, or managing niche risks. These agents excel in their designated areas, refining their decision-making through reinforcement learning and stochastic control principles
Coordinate the outputs of junior agents, balancing their insights to align with overarching objectives like long-term strategy, portfolio diversification, or risk management.
This hierarchy mirrors the structure of a quant fund, where individual contributors focus on specific areas, and higher-level decision-makers synthesize information to guide the entire system.
Our agents are designed to collaborate and compete dynamically, sharing insights and refining strategies in real time:
Agents exchange information, such as probabilistic mappings of their
environments, to ensure alignment with team goals.
Agents are encouraged to explore and test alternative strategies, pushing the boundaries of their learning to uncover new opportunities.
This interplay creates a balance between specialization and adaptability, enabling the system to respond effectively to changing market conditions.
This modular design of our coordinated agents and innovative approach allows us to address intricate
decision-making processes with precision and scalability.
New agents can be introduced seamlessly to handle emerging challenges or opportunities.
Existing agents can adapt to new roles or priorities without disrupting the overall system.
High-level coordinating agents maintain system-wide balance, ensuring efficiency and alignment with long-term objectives.
By mapping control problems onto a hierarchy that mirrors real-world quant fund operations, our coordinated agent system delivers a robust and intuitive solution to managing complexity. This architecture enables us to tackle uncertainty with precision, adapt dynamically to change, and
maintain a unified focus on the big picture—all while leveraging the power of advanced AI techniques like stochastic control theory and reinforcement learning.
With coordinated agents, we transform complexity into clarity, ensuring optimal decision-making at every level of the system.
