test-strategy

You can test Agentic systems without real LLM API calls by using mock LLM clients to simulate interactions. This Skill automates setting up these mock clients to streamline your TDD workflow and eliminate API costs during testing.

Property-based testing controls non-determinism in LLM testing by defining invariant properties that outputs must satisfy across generated inputs. This Skill implements property-based testing using the hypothesis framework to validate Agentic system behavior.

The best way to automate golden dataset evaluations for TDD is to compare system outputs against predefined expected results. This Skill automates these evaluations to ensure consistent quality and verify Agentic system performance without manual intervention.

Yes, this TDD strategy works with pytest and hypothesis for Agentic systems. It also integrates with pytest-anyio for asynchronous testing and vcrpy for recording HTTP interactions, providing a comprehensive automated testing environment.

Yes, you can use mock LLM clients to test Agentic systems built with Anthropic and OpenAI. The Skill dependencies include the anthropic and openai libraries, allowing you to simulate interactions for both platforms during test-driven development.

You need property-based testing for Agentic systems when dealing with non-deterministic outputs that require robust validation. This Skill implements property-based testing to control non-determinism and ensure your system maintains expected behavior across varied inputs.