Testing Strategies

Unit Testing
Test individual agent components, tools, and functions in isolation with mocked dependencies and controlled inputs to verify correct behavior.
Component Isolation Fast Execution Regression Detection
Integration Testing
Test agent interactions with external systems, APIs, databases, and other agents to ensure proper integration and data flow.
System Integration API Validation Data Flow Testing
Conversation Testing
Test multi-turn conversations, context maintenance, and response quality using predefined scenarios and expected outcomes.
Context Validation Response Quality Flow Testing
Performance Testing
Test agent response times, throughput, memory usage, and scalability under various load conditions and concurrent users.
Load Testing Scalability Resource Usage
Security Testing
Test for prompt injection, data leakage, unauthorized access, and other security vulnerabilities specific to AI agent systems.
Prompt Injection Data Protection Access Control

Debugging Techniques

Conversation Tracing
Track conversation flow, decision points, and state changes throughout agent interactions.
Example: Trace conversation history, tool calls, and reasoning steps
Performance Profiling
Analyze response times, token usage, API calls, and resource consumption patterns.
Example: Profile LLM API latency, memory usage, and processing bottlenecks
Error Analysis
Systematic analysis of failures, exceptions, and unexpected behaviors with root cause identification.
Example: Analyze tool execution failures, parsing errors, and timeout issues
State Inspection
Examine agent internal state, memory contents, and context at specific points in execution.
Example: Inspect conversation memory, tool results, and decision variables
Response Validation
Validate agent responses against expected formats, content quality, and business rules.
Example: Check response format, factual accuracy, and policy compliance