Test Case

A test case describes the complete process of running your AI agent evaluations, which involves two primary phases: Simulation and Evaluation.

This approach ensures comprehensive testing by combining agents, scenarios, personas, and metrics into structured evaluation frameworks that measure performance against defined success criteria.

Simulation Phase

The simulation phase generates realistic conversation interactions between personas and your AI agent:

Simulation Generation

Each unique combination of scenario and persona creates an individual simulation:

Total simulations: Scenarios + Personas = Individual test cases.
Parallel execution: Multiple simulations run simultaneously for efficiency.
Comprehensive coverage: Every scenario is tested with every selected persona.

Conversation Execution

During simulation, personas follow their defined behavioral patterns while engaging with scenarios:

Realistic interactions: Personas communicate according to their personality traits.
Scenario adherence: Conversations follow the defined situation and user goals.
Complete recording: All audio and conversation data is captured for analysis.

Evaluation Phase

The evaluation phase assesses simulation results against defined success criteria:

Performance Measurement

Each simulation is measured against all configured metrics:

Technical metrics: Automatic assessment of latency, duration, and quality.
Semantic metrics: AI-powered evaluation of conversation understanding and goal completion.
Pass/fail determination: Results compared against defined thresholds.

Analysis Generation

A comprehensive analysis is created from evaluation results:

Individual simulation analysis: Detailed breakdown of each conversation.
Pattern identification: Recognition of common success factors and failure points.
Improvement recommendations: AI-generated suggestions based on performance data.

Testing Workflow

A complete test case follows this pattern:

Setup: Create a test suite with appropriate agents, scenarios, personas, and metrics.
Simulation: Launch test suite to generate realistic conversation interactions.
Evaluation: Assess simulation results against defined success criteria.
Analysis: Review performance data and identify improvement opportunities.
Implementation: Apply changes based on findings and recommendations.
Validation: Re-run the test suite for each test case to verify improvements and measure progress.

The test case approach ensures that your AI agent testing captures realistic user interactions and objective performance measurement, providing the foundation for agent improvement and quality assurance.