Golan focused on improving the reliability and clarity of the Shopping Assistant AI Agent in the ppekrol/ravendb repository. He addressed a persistent flaky test in the agent’s configuration, stabilizing automated test outcomes and reducing noise during development. Using C# and leveraging his skills in AI development and unit testing, Golan enhanced the agent’s output schema to include additional properties, enabling more robust response handling for downstream systems. He also refined the agent’s purpose description to better align with intended behaviors. The work demonstrated careful attention to test stabilization, schema design, and prompt handling, contributing to safer and faster automation.