Lesson 3: Specialized Agent with Stock Sentiment Analysis

This lesson creates a specialized agent focused on stock sentiment analysis using the Agent Framework with specific system instructions and function calling capabilities.

1. Copy the configuration file from the Solutions directory:

   mv ../Lesson1/appsettings.json appSettings.json
   

   cp ../Lesson1/appsettings.json appSettings.json
   

2. Open Program.cs and build a specialized stock sentiment agent:

   1. TODO: Step 1 - Define specialized system instructions for stock sentiment analysis:

      string stockSentimentAgentInstructions = """
          You are a Stock Sentiment Agent. Your responsibility is to find the stock sentiment for a given Stock.
      
          RULES:
          - Use stock sentiment scale from 1 to 10 where stock sentiment is 1 for sell and 10 for buy.
          - Provide the rating in your response and a recommendation to buy, hold or sell.
          - Include the reasoning behind your recommendation.
          - Include the source of the sentiment in your response.
          - Focus on technical analysis based on stock price data and general market knowledge.
          """;
      

   2. TODO: Step 2 - Create the specialized Stock Sentiment Agent:

      ChatClientAgent agent = new(
          chatClient,
          instructions: stockSentimentAgentInstructions,
          name: "StockSentimentAgent",
          description: "An intelligent agent that analyzes stock sentiment using market data",
          tools: tools
      );
      

3. Test the specialized agent with stock sentiment queries:

   dotnet run
   

   Example questions to test:

   User > What is your sentiment on MSFT?
   Assistant > (should analyze Microsoft stock and provide sentiment rating 1-10 with buy/hold/sell recommendation)
   
   User > Analyze AAPL sentiment
   Assistant > (should provide Apple stock sentiment analysis with reasoning)
   
   User > Should I buy TSLA?
   Assistant > (should analyze Tesla and provide specific buy/hold/sell recommendation)
   

This lesson demonstrates how to create specialized agents with focused system instructions and domain-specific capabilities using the Agent Framework.