Step-by-Step Guide to Building a Portfolio Manager: A Multi-Agent System with Microsoft Semantic Kernel and Azure OpenAI

In this guide, we will walk through how to create a portfolio manager using a multi-agent system with Microsoft Semantic Kernel and Azure OpenAI. The key aspect we will focus on is the selection function in Semantic Kernel, which helps define how agents collaborate. We will use the example of a 'Portfolio Management Multi-Agent System' to demonstrate this.

Selection Function in a Multi-Agent System

The selection function in Semantic Kernel defines the rules for agent collaboration, preventing incorrect loops and ensuring consistent output. We will illustrate how this function works with the following three agents in our portfolio management system:

1. Portfolio Optimizer Agent
2. Web Surfer Agent
3. Stock Analyzer Agent

Step-by-Step Guide:

Step 1: Define Agents

• Create the three agents mentioned above with their respective roles and functions.

Step 2: Define Selection Strategy

• Establish the selection strategy that dictates how agents collaborate and contribute to the overall portfolio management process.

Step 3: Define Termination Strategy

• Specify the termination strategy to determine when to stop collaboration between agents based on predefined conditions.

Step 4: Create Useful Tools

• Develop tools such as NewsAPI for retrieving the latest news and GetPortfolio for accessing the user's up-to-date portfolio information.

Step 5: Creating Multi-Agent Chat

• Implement a multi-agent chat system where the agents can communicate and share information, such as the user's portfolio details.

By following these steps, you can build a robust portfolio manager that leverages the power of a multi-agent system, Microsoft Semantic Kernel, and Azure OpenAI. The collaboration between agents facilitated by the selection function ensures efficient and accurate portfolio optimization and management.

This guide provides a framework for designing and implementing complex systems that require intelligent agent interactions, offering a practical approach to enhancing decision-making processes in various domains.