Problem Statement:
The organization needed an efficient solution to manage interactions between advisors and players, specifically addressing the issue of updating client email information. The existing process required automation to ensure smooth communication and accurate resolution of player inquiries using Assistants and WebSocket API.
Solution:
Development and Implementation Automation of WebSocket API:
Developed a Python Framework to automate the interaction process using WebSocket API.
Sent a series of requests from a JSON file to simulate real user interactions.
Response Assertion:
Designed a mechanism to assert responses from the WebSocket API.
Utilized instructions provided in a text file to verify the accuracy of responses.
If the WebSocket response matched the advisor’s instructions, the result was marked as “pass”; otherwise, it was marked as “fail.”
Load Testing:
Conducted load testing on the WebSocket API to evaluate response time and system performance under stress.
Analyzed performance metrics to ensure reliability and scalability of the system.
Analysis and Results:
Accuracy and Reliability:
- Achieved high accuracy in automating advisor-player interactions.
- Ensured that responses consistently met the defined criteria for successful resolution.
Performance Metrics:
- Load testing demonstrated the API’s ability to handle multiple requests efficiently.
- Identified optimal response times and system thresholds for future improvements.
Conclusion:
The implementation of the Feature Advisor Assist automated system successfully streamlined advisor-player interactions, improving the process of updating client email information. By leveraging WebSocket API automation and precise response assertion, the organization enhanced the efficiency and accuracy of its support services.
Future Recommendations:
Continuous Improvement:
- Regularly update the automation scripts to adapt to new interaction patterns and advisor guidelines.
Scalability:
- Explore options for scaling the system to accommodate increased user interactions and complex queries.
Feedback Integration:
Implement a feedback loop to gather user insights and refine the system further.
