Conversational Agents Integration Issues

[EBastiani]

I'm currently exploring the Conversational Agents feature in Agent Builder and have encountered issues with response times when integrated with Slack. While interactions within the Conversational Agents UI are seamless, including smooth transitions between playbooks, the Slack integration often results in delayed responses and difficulties in switching playbooks.

Considering that Conversational Agents are still in preview, I'm seeking insights into potential causes for these performance issues and strategies to enhance the agent's responsiveness within Slack integrations.

Additionally, are there best practices or configurations recommended to optimize the performance of Conversational Agents in Slack?

For context, I have followed the standard integration procedures as outlined in the official documentation.

Any guidance or shared experiences with similar challenges would be greatly appreciated.

Thank you in advance for your assistance.

[MarvinLlamas]

Hi @EBastiani,

Welcome to Google Cloud Community!

It looks like you're encountering performance issues with Conversational Agents in Slack. These include slow response times and difficulties with playbook switching.

Here are potential ways that might help with your use case:

• Track Performance: You might want to consider that identifying bottlenecks is essential for effective troubleshooting. Without monitoring, you're merely guessing. Pay attention to extended steps in your playbooks, API call delays, and resource limitations within your Agent Builder environment.
• Enhance API Calls: You may want to optimize your API calls by streamlining them to retrieve only essential data. This addresses a major potential cause of delays—the time it takes to fetch and process data. By doing so, you reduce the load on both your agent and the external services it interacts with.
• Caching: You may want to implement caching strategies to minimize the number of API calls and improve response times. This approach significantly benefits repeat requests and common data points, effectively preventing your agent from performing the same costly operations repeatedly.
• Load Balancing: You may want to implement load balancing to ensure even traffic distribution across servers. If server capacity is the bottleneck after optimizing API calls and playbooks, load balancing is crucial. This might require infrastructure changes and may not always be manageable within your Agent Builder.
• Feedback Mechanism: You might want to create a feedback system to gather user input to enhance your agent's performance, identify and optimize slow response cases, and find edge cases affecting your agent's performance.

You may refer to the following documentation, which might help you understand how to diagnose the root cause of your issues with the integration of Dialogflow and Slack. The guidance will help you determine if your implementation uses Dialogflow ES or CX, which is crucial for effective troubleshooting:

• Slack | Dialogflow CX
• Slack | Dialogflow ES

Was this helpful? If so, please accept this answer as “Solution”. If you need additional assistance, reply here within 2 business days and I’ll be happy to help.

[EBastiani]

I'll try that out. Thank you!