Testing and Debugging

Thorough testing and debugging are essential steps in ensuring your AI agents perform as intended before going live. This process involves evaluating both the final responses provided to users and the workflow sequences that lead to those responses. By systematically testing and refining your agents, you can identify and address issues, ensuring a seamless user experience.​

1. Utilize the Platform's Integrated Debugging Features

The indigo.ai platform includes a powerful debugging tool that helps you understand how each message in the chat is generated. It shows you a clear and detailed log of what happens during the conversation, including:

• The workflows the assistant went through

• Which agents were involved

• The knowledge base content (document chunks) used to answer the user

• What triggered the start of the flow

• Any reroutes or decisions made along the way.

By reviewing these elements, you can quickly spot where things aren't working as expected and make the right changes to your workflows or agent setup.

Learn more about how to use the debugging feature in detail in the next article: Debugging.

2. Build Testing into Your Workflow Design

Incorporating testing mechanisms directly into your workflow design can streamline the debugging process. Consider implementing the following best practices:​

• Conditional Debugging Messages - Use the Condition Block to check if the environment is set to "test." If true, configure the workflow to display specific variable values or fixed text messages during the conversation. This approach helps validate variable values and ensures that the workflow behaves as expected in the test environment.​

• Prompt Blocks with Reasoning Variables - When using prompts that return JSON to extract information from user input, it’s helpful to include a "reasoning" key in the response. This key offers visibility into how the AI agent made its decision. To make debugging easier during testing, you can add a Text Block that displays the value of the reasoning variable—just condition it to show only in the test environment. This gives you a clearer view of what’s happening behind the scenes and helps troubleshoot unexpected behavior. For detailed guidance on utilizing reasoning in prompts, refer to this article: 🧠 Prompt Block.

3. Testing in a Staging Environment

While the platform's Preview feature is ideal for quick, individual tests, involving others in a structured staging environment allows you to uncover issues from diverse perspectives and simulate real-world usage more accurately.

To enable collaborative testing before going live, you can share controlled access to your assistant in two ways:

• Staging Installation: Install the assistant in a staging environment using the test token to replicate production-like conditions.

• Full-Page Preview: Alternatively, generate a focused testing link by appending the chat token to this URL: https://platform.indigo.ai/chatbot/preview/. This creates a full-page interface ideal for streamlined external testing.

4. 🆕 Use the Issue Tracker for Collaborative Testing

The feature is currently in beta. Read more about it in the dedicated article: Issue Tracker for Tests & Improvements

Manual testing using the platform’s preview feature is a great starting point, but the real power of structured, collaborative testing lies in the Issue Tracker.

The Issue Tracker replaces the need for external spreadsheets by allowing testers to flag and document issues directly within the platform.

Here’s how to use it:

• Flag issues from Chats or the AI Preview: During testing or review, hover over any message to create an issue. You can specify a title, description, priority, tags (e.g., Bug, Improvement), and assign it to a team member or your indigo.ai Customer Success Manager.

• Dashboard View: All flagged issues are collected in a centralized dashboard where you can sort, filter, and manage them based on priority, tag, assignee, or status.

• Comment and Track Progress: Use the dashboard to leave comments, track status updates, and ensure every issue is followed through to resolution.

This built-in system brings structure and speed to your testing process, making it easier to collect and act on feedback.

5. Implement Feedback Collection Workflows (For Advanced Users)

If you want to gather feedback from users during their interaction with the virtual assistant, directly in the chat, you can create a dedicated workflow for that.

How it works:

1. Feedback Workflow: Create a feedback request workflow composed of a quick reply block asking users to rate the interaction with a 👍 or 👎. If the user selects 👎, ask them to provide additional details about what didn’t work or what they were expecting instead.

2. Feedback Workflow Routing: At the end of the conversation flow you wish to evaluate, include a reroute block to direct users to the feedback collection workflow.​

3. Data Integration: Utilize integrations with tools like Zapier (note: requires an account) to automatically save feedback data into a Google Sheet or another database. Zapier can capture various parameters, including the conversation transcript, timestamp, and user-provided feedback, compiling them into a structured format for analysis.​

Things to Keep in Mind:

• User Experience Considerations: Be mindful that interrupting the conversation to request feedback can disrupt the user experience. Use this approach selectively to avoid diminishing user engagement.​

• Complex Setup: Integrating with external tools like Zapier involves additional configuration and maintenance. If you require assistance setting up this integration, please reach out for guidance.​