Structured Evaluation with Testaify

Design a Testaify test suite for your 'GlobalTaxAdvisor' agent focusing on the 'LegalComplianceQuery' task. Define a few test cases, each with an input query, country, and the expected 'advice', 'is_compliant' status, and list of 'citations'. Describe how Testaify would run these tests and generate reports on the agent's performance, including specific assertion checks for correctness and completeness. Provide a conceptual Python structure for setting up these tests.

```python
# Conceptual Testaify usage (Testaify is an example tool for structured eval)
# from testaify import TestSuite, TestCase, assert_equals, assert_contains

# class GlobalTaxAdvisorTestSuite(TestSuite):
#     def setup(self):
#         self.advisor_agent = initialize_openai_assistant()

#     @TestCase(name="German Corporate Tax Query")
#     def test_german_corporate_tax(self):
#         input_data = {"query": "corporate tax in Germany", "country": "Germany", "context": "small business"}
#         agent_output = self.advisor_agent.run(input_data)
#         assert_equals(agent_output['is_compliant'], True)
#         assert_contains(agent_output['advice'], "15%")
#         assert_contains(agent_output['citations'], "German Corporate Tax Act")

#     @TestCase(name="UK Income Tax Brackets")
#     def test_uk_income_tax(self):
#         input_data = {"query": "income tax brackets UK", "country": "UK", "context": "individual"}
#         agent_output = self.advisor_agent.run(input_data)
#         assert_equals(agent_output['is_compliant'], True)
#         assert_contains(agent_output['advice'], "basic rate")

# if __name__ == '__main__'
#     TestSuite.main(GlobalTaxAdvisorTestSuite)
```