Testing Strategy

Testing Strategy for AI Agents

Unit Testing

• Test each @tool function independently with mocked dependencies
• Test hook middleware (before/after invoke callbacks)
• Test memory backend operations (create_event, retrieve_records)
• Test session ID generation and namespace construction
• Use pytest fixtures for memory/tool mocking

Integration Testing

• Test agent end-to-end with real model but mocked tools
• Test memory round-trip: write → extract → retrieve
• Test Slack handler → AgentCore invoke → response pipeline
• Test session isolation (two users, verify no data leak)
• Test cold start and lazy initialization

Agent Evaluation

• Define golden test cases: input → expected behavior/output
• Test for: helpfulness, accuracy, safety, latency
• Evaluate memory usage: does the agent save/recall correctly?
• Test skill loading: does the agent pick the right skill?
• Test guardrails: does the agent refuse harmful requests?

Memory Testing

• Verify STM events created for each conversation turn
• Verify LTM extraction after strategy processing
• Test namespace isolation (actor A can't see actor B)
• Test memory context loading on session resume
• Test explicit save_memory / recall_memory tools

Mocking Patterns

# Mock AgentCore Memory
@patch("platform_agent.memory.AgentCoreMemory")
def test_memory_save(mock_memory):
    mock_memory.add_assistant_message.return_value = "evt-123"
    # ... test save_memory tool

# Mock Bedrock model for agent tests
@patch("strands.models.bedrock.BedrockModel")
def test_agent_response(mock_model):
    mock_model.return_value = mock_streaming_response("Hello!")
    # ... test agent invocation