What is the correct way to handle dynamic skill assignment for AI-driven shift swaps in WFM?

Is there a clean way to handle dynamic skill assignment for AI-driven shift swaps in WFM?

Our team is trying to integrate a new AI-based recommendation engine for shift swaps to improve agent satisfaction and adherence. We are using the WFM Schedule API (v2) to publish schedules and handle swap requests. The goal is to automatically assign or remove skills based on the swap outcome, ensuring that the incoming agent has the required skills for the shift. However, we are encountering issues where the skill assignments are not updating correctly in real-time. When an agent requests a swap, the system should check the incoming agent’s skill profile and update the schedule accordingly. If the incoming agent lacks a required skill, the swap should be flagged for manual review. Instead, the API returns a 409 Conflict error with the message: “Skill mismatch detected for shift ID 12345.” We have verified that the skill profiles are correct in Admin > Workforce Management > Skills. The issue seems to occur when the swap involves a shift with multiple skill requirements. We are using the /api/v2/wfm/schedules/swap endpoint to process the swap request. The payload includes the swap details, agent IDs, and shift IDs. We have also tried using the /api/v2/wfm/schedules/validate endpoint to pre-validate the swap before submission, but it does not catch the skill mismatch issue. The environment is Genesys Cloud, and we are using the latest SDK version. The timezone is America/Chicago. We have checked the logs and found that the skill validation is happening after the swap request is processed, which causes the conflict. We need a way to pre-validate the skill requirements before processing the swap request. Has anyone faced a similar issue or have a workaround? We are looking for a solution that ensures real-time skill validation and prevents swap conflicts. Any insights or best practices would be greatly appreciated. We are also open to suggestions on how to improve the integration between the AI recommendation engine and the WFM API.