## 2 Foundational Concepts and Technologies
### Multi-Agent Systems
#### 2.3.1 Agent Engineering  
The design of effective agent systems requires consideration of collaboration mechanisms, scaling approaches, and task decomposition strategies [32]. In agent collaboration, two primary architectures have emerged: hierarchical and sequential processing. In hierarchical collaboration, agents communicate freely and process inputs in a network-like structure, while sequential processing involves agents following a structured sequence to build upon each other's outputs [33]. Our system adopts the hierarchical approach as illustrated in Figure 8. This allows for asynchronous execution of tasks and significantly accelerates the prediction process. This choice is motivated by the observation that hierarchical structures enable more flexible communication patterns and can better handle complex, interdependent tasks.  
The first component of effective multi-agent system is dynamic scaling, which adjusts the number of active agents based on task complexity and available resources [33]. This approach ensures optimal resource utilization and maintains system performance under varying conditions. The system can autonomously increase or decrease the number of agents, ensuring that computational resources are allocated efficiently while maintaining the quality of outputs [35]. Dynamic scaling has proven particularly valuable in handling tasks of varying complexity, where the required computational resources may fluctuate significantly.  
The decomposition of large tasks into fewer smaller ones serves as a fundamental strategy in agent engineering, enabling the breakdown of complex tasks into smaller, manageable sub-tasks [36]. In hierarchical task decomposition, tasks are organized into a structured hierarchy where each level can be further decomposed until reaching a granularity suitable for individual agents [7]. This method ensures that specialized agents can handle specific parts of a task, optimizing overall efficiency. Task decomposition not only improves the system's ability to handle complex problems but also enhances the clarity of agent responsibilities and improves overall system maintainability.  
Memory management represents another component in agent engineering [32; 37]. Effective agents must maintain both short-term memory for immediate context and task-related information and long-term memory for storing substantial volumes of knowledge and past experiences. The integration of these memory systems enables agents to learn from previous interactions and improve their decision-making processes over time. This is particularly important in financial analysis tasks, where historical context and pattern recognition are used for future analyses.  
The integration of RAG and DRL has further enhanced agent capabilities. RAG improves the accuracy and reliability of agent outputs by enabling access to external knowl  
Figure 8: Diagram of supervisor agent in multi-agent hierarchical architecture, adapted from Ref. [34].  
edge sources [12], while DRL enables agents to learn and adapt from their experiences [38]. These technologies, combined with dynamic context management, allow agents to maintain relevance and accuracy throughout their operational life cycle.  
The successful engineering of AI agent systems requires careful orchestration of multiple components and considerations. From collaboration architectures and dynamic scaling to memory management and tool integration, each aspect plays a crucial role in creating efficient systems. As the field continues to evolve, the integration of advanced technologies like RAG and DR will become increasingly important.