IEEE AI Standards for Agentic Systems
Richard Jiarui Tong, Haoyang Li, Sridhar Raghavan, Qingsong Wen, Shannon Gray, Anand Paul, Joleen Liang, Janusz Zalewski, Yacheng Yang, George Tambouratzis, Bong Chong Ang · 2025
This paper synthesizes key insights from emerging IEEE Artificial Intelligence Standards Committee (AISC) standards - P3394 and P3428 - that are shaping agent-based software engineering for intelligent systems. IEEE P3394 (LLM Agent Interface) defines a Universal Message Format (UMF) and communication protocols for Large Language Model (LLM) agents, establishing standard message envelopes, semantic payload, agent roles, session management, and interaction patterns. IEEE P3428 (LLM Agents for Education) specifies a modular agent architecture and lifecycle tailored to adaptive learning environments. It standardizes agent components, lifecycle states, and orchestration mechanisms to enable plug-and-play integration of multiple AI-driven agents in an adaptive instructional system. Together, P3394 and P3428 promote modular, interoperable, and scalable design of intelligent agent ecosystems. We highlight how P3394's universal message protocols and P3428's standardized agent lifecycle complement each other in supporting LLM-based agents and agent-based intelligent systems. We also briefly discuss IEEE P3427, an initiative on semantic information agents, which underscores the broader context of evaluation and continuous improvement of agent-based systems. By unifying communication interfaces and architectural frameworks, these standards lay a foundation for next-generation agentic systems that can seamlessly interoperate across platforms and domains.