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在 Connect 中通过 Amazon Q 使用人工智能驱动的生成式自助服务

Amazon Q in Connect 支持聊天和语音 (IVR) 渠道中的客户自助服务用例。它可以：

当客户需要其他帮助时，Amazon Q in Connect 会将其无缝转移给客服，同时保留完整对话的背景。

默认系统工具

Amazon Q in Connect 附带了以下可用的内置工具 out-of-the-box：

您可以自定义这些默认工具以满足您的特定要求。

设置自助服务

请按照以下步骤启用 Amazon Q in Connect 进行自助服务：

自助服务的自定义操作

您可以通过添加自定义工具来扩展 Amazon Q in Connect 的功能。这些工具可以：

在 AI 提示中添加自定义工具时：

示例：消除客户意图的歧义

您可以创建一个生成式 AI 助手，用于在路由到代理之前收集信息。这需要：

以下是用于消除歧义的工具定义示例。你可以移除除 CONVERSATION 之外的所有默认工具，然后添加一个名为 HANDOFF 的新自定义工具：

tools:
- name: CONVERSATION
  description: Continue holding a casual conversation with the customer.
  input_schema:
    type: object
    properties:
      message:
        type: string
        description: The message you want to send next to hold a conversation and get an understanding of why the customer is calling.
    required:
    - message
- name: HANDOFF
  description: Used to hand off the customer engagement to a human agent with a summary of what the customer is calling about.
  input_schema:
    type: object
    properties:
      message:
        type: string
        description: Restatement to the customer of what you believe they are calling about and any pertinent information. MUST end with a statement that you are handing them off to an agent. Be as concise as possible.
      summary:
        type: string
        description: A list of reasons the customer has reached out in the format <SummaryItems><Item>Item one</Item><Item>Item two</Item></SummaryItems>. Each item in the Summary should be as discrete as possible.
    required:
    - message
    - summary

示例：向客户推荐一项操作

您可以使用流程在中 Amazon Connect 配置下一个最佳操作。您还可以配置自动操作并创建 step-by-step指南，为客户提供基于用户界面的操作。有关更多信息，请参阅 Step-by-step 设置 Amazon Connect 代理工作空间的指南。  Connect 中的 Amazon Q 将选定的工具名称保存为 Lex 会话属性。然后可以使用 “检查联系人属性” 流程块访问该属性。 

以下是预订行程的工具定义示例：

name: TRIP_BOOKING
  description: Tool to transfer to another bot who can do trip bookings. Use this tool only when the last message from the customer indicates they want to book a trip or hotel.
  input_schema:
    type: object
    properties:
      message:
        type: string
        description: The polite message you want to send while transferring to the agent who can help with booking.
    required:
    - message

使用 “检查联系人属性” 流程块来确定 Amazon Q in Connect 选择了哪个工具时，您可以做出分支决定，为该用户选择相关 step-by-step指南。例如，如果客户想在自助聊天互动中预订行程，您可以：

有关在聊天中实现 step-by-step指南的更多信息，请参阅在 Amazon Connect 聊天中部署 step-by-step指南。

跟进问题工具

FOLLOW_UP_QUESTION 工具支持与客户进行更具互动性和信息收集的对话，从而增强了 Amazon Q in Connect 的自助服务功能。此工具可与默认工具和自定义工具一起使用。它有助于在确定要采取的行动之前收集必要的信息。

以下代码显示了 FOLLOW_UP_QUESTION 工具的配置。

- name: FOLLOW_UP_QUESTION
  description: Ask follow-up questions to understand customer needs, clarify intent, 
    and collect additional information throughout the conversation. Use this to gather 
    required details before selecting appropriate actions.
  input_schema:
    type: object
    properties:
      message:
        type: string
        description: The message you want to send next in the conversation with the 
          customer. This message should be grounded in the conversation, polite, and 
          focused on gathering specific information.
    required:
      - message

FOLLOW_UP_QUESTION 工具使得 Amazon Q in Connect 能够在决定采取何种操作之前收集必要的信息，从而补充您定义的工具。它对以下方面特别有用：

FOLLOW_UP_QUESTION

对于旨在举报欺诈的自助机器人，您可以定义一个名为 CONFIRM_SUBMITION 的工具来收集客户的特定信息：

- name: CONFIRM_SUBMISSION
  description: Confirm all collected information and finalize the report submission.
  input_schema:
    type: object
    properties:
      message:
        type: string
        description: A message reviewing all of the collected information and asking 
          for final confirmation before submission.
      report_details:
        type: string
        description: The user's report or complaint details
      reporter_info:
        type: string
        description: Reporter's contact information (if provided) or "Anonymous"
      subject_info:
        type: string
        description: Information about the individual or business being reported
    required:
      - message
      - report_details
      - reporter_info
      - subject_info

但是，您可以改用 FOLLOW_UP_QUESTION 工具来收集这些信息 step-by-step，如以下示例所示：

- name: FOLLOW_UP_QUESTION
  description: Ask follow-up questions to understand customer needs and collect additional 
    information throughout the complaint process. Use this for all information gathering 
    steps including confidentiality preferences, contact info, subject details etc.
  input_schema:
    type: object
    properties:
      message:
        type: string
        description: The message you want to send next in the conversation with the 
          customer. This message should be grounded in the conversation and polite. 
          Use this for asking clarification questions, collecting contact information, 
          gathering subject details, and all other follow-up steps in the complaint 
          process.
    required:
      - message

提示指令

在提示中添加说明，指导您的自助服务机器人何时使用 FOLLOW_UP_QUESTION 工具。例如：

CRITICAL: Use FOLLOW_UP_QUESTION for all information gathering steps after the initial analysis. 
Do NOT proceed to other tools until you have collected all required information. Use this tool 
to disambiguate customer intent when unclear.

When using FOLLOW_UP_QUESTION:
1. Ask one specific question at a time
2. Focus on collecting required information for the most likely intent
3. Be conversational but direct
4. Acknowledge information the customer has already provided
5. Only move to other tools (like CONFIRM_SUBMISSION or TRIP_BOOKING) when you have all required 
   information

对话示例

以下是三个示例对话，它们显示了正在使用的 FOLLOW_UP_QUESTION 工具。

<examples>
    <example>
        <conversation>
            [CUSTOMER] I want to report someone who I think is committing fraud
        </conversation>
        <thinking>
            The customer wants to report fraud. I need to collect specific information 
            including details about the suspected fraud, reporter information, and 
            subject information. I'll start by asking about the suspected fraudulent activity.
        </thinking>
        <tool>
            [FOLLOW_UP_QUESTION(message="I'd be happy to help you report suspected fraud. 
            Could you please describe the fraudulent activity you've observed?")]
        </tool>
    </example>
    
    <example>
        <conversation>
            [AGENT] Can you provide me your full address?
            [CUSTOMER] I don't feel comfortable sharing my address
        </conversation>
        <thinking>
            The caller declined to share their address. I should respect their privacy 
            concerns while still trying to collect alternative contact information.
        </thinking>
        <tool>
            [FOLLOW_UP_QUESTION(message="I understand your privacy concerns. That's 
            completely fine. Would you be comfortable providing a phone number or email 
            address instead? Or you can choose to remain anonymous.")]
        </tool>
    </example>
    
    <example>
        <conversation>
            [CUSTOMER] I'm not getting my benefit payments anymore
        </conversation>
        <thinking>
            The customer is reporting a payment issue. This sounds like a general service 
            issue rather than fraud. I should clarify if they need assistance with their benefits.
        </thinking>
        <tool>
            [FOLLOW_UP_QUESTION(message="I'm sorry to hear about the issue with your payments. 
            To help me understand better, could you tell me when you last received a payment 
            and if you've received any notification about why they stopped?")]
        </tool>
    </example>
</examples>