Use Case: Automated AI Agent Testing
For AI solution providers, ensuring that AI-driven agents perform consistently and reliably in real-world scenarios is critical. Manual testing can be time-consuming, labour-intensive, and prone to missed issues. The Automated AI Agent Testing Solution leverages one Occamise agent to test another, simulating real-world interactions through automated outbound calls. By automating these tests, QA engineers and AI solution teams can save time, identify potential issues early, and maintain high-quality standards across AI systems.
Solution overview
The Automated AI Agent Testing Solution is designed to test the functionality of AI agents through realistic, hands-free simulations. Using an Occamise agent, the system initiates outbound calls to the AI agent being tested, performing a series of preconfigured test scenarios. These tests assess the agent’s performance against defined criteria, ensuring adherence to expected processes and correct responses.
A dedicated Configuration Agent enables users to set up new test cases, define success criteria, and execute test runs. By automating test execution and allowing for simultaneous test case initiation, QA teams can obtain a comprehensive view of all test results within minutes, significantly accelerating the testing process and enabling rapid adjustments when issues are detected.
Key features and capabilities
Automated Outbound Call Testing: Initiates outbound calls to AI agents, simulating real-world scenarios to test various functionalities and responses.
Performance Assessment: Compares the tested agent’s responses against predefined success criteria, identifying any deviations or errors.
Configuration Agent: Allows QA teams to create, modify, and organise test cases, define success parameters, and initiate test runs with ease.
Simultaneous Test Execution: Triggers multiple test cases virtually simultaneously, providing a complete test report within minutes.
Detailed Reporting and Analysis: Offers a comprehensive report of each test case, including pass/fail status, response accuracy, and adherence to configured processes.
Expected benefits
Reduced manual testing time, allowing for faster QA cycles and quicker deployment
Improved quality control across AI-driven systems by ensuring consistent functionality and accurate responses
Greater testing coverage by enabling simultaneous execution of multiple test scenarios
Comprehensive reporting that helps QA teams identify and address potential issues early
Real-world example
A QA engineer at a company providing customer support solutions configures test scenarios to verify the performance of a voice AI agent responsible for handling customer inquiries. The Automated AI Agent Testing Solution initiates multiple test cases, each simulating different customer interactions, such as order status inquiries and troubleshooting requests. Within minutes, the engineer receives a detailed report on the agent’s performance, showing whether each test case passed or failed, response accuracy, and any deviations from expected behaviour. This allows the QA team to quickly identify areas for improvement, ensuring the agent is ready for deployment.