This recommended practice describes an architectural framework and application domain for Artificial Intelligence (AI) agents in the field of talent services. The recommended practice addresses a) how perception, cognition, and behavior are integrated into the AI agent design process; b) protocols used for interaction and communication mechanisms between agents and users like talent service practitioners, and between multiple agents (e.g., message exchanges, negotiation strategies); and c) methods to evaluate AI agent systems for use in talent services, including metrics and benchmarks. These methods measure the effectiveness, robustness, and ethical application of single agent and multi-agent ecosystems.
- Standard Committee
- C/SABSC - Standards Activities Board Standards Committee
- Status
- Active PAR
- PAR Approval
- 2025-03-27
Working Group Details
- Society
- IEEE Computer Society
- Standard Committee
- C/SABSC - Standards Activities Board Standards Committee
- Working Group
-
TSMWG - Talent Service and Management Working Group
- IEEE Program Manager
- Jonathan Goldberg
Contact Jonathan Goldberg - Working Group Chair
- Hengshu Zhu
Other Activities From This Working Group
Current projects that have been authorized by the IEEE SA Standards Board to develop a standard.
No Active Projects
Standards approved by the IEEE SA Standards Board that are within the 10-year lifecycle.
3154-2024
IEEE Recommended Practice for the Application of Knowledge Graphs for Talent Services
Talent is critically important for the success of companies, so a large number of talent services have developed over time. Institutions and enterprises are now implementing knowledge graph technology as part of Artificial Intelligence systems to provide more accurate and interpretable intelligent talent services. This recommended practice assists developers in constructing knowledge graphs in the field of talent services more efficiently and consistently. In addition, it provides a general implementation method for institutions and enterprises to use knowledge graphs in different application scenarios, such as talent recruitment and development.
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