AEMA: Verifiable Evaluation Framework for Trustworthy and Controlled Agentic LLM Systems

• URL: http://arxiv.org/abs/2601.11903v1
• Date: Sat, 17 Jan 2026 04:09:02 GMT
• Title: AEMA: Verifiable Evaluation Framework for Trustworthy and Controlled Agentic LLM Systems
• Authors: YenTing Lee, Keerthi Koneru, Zahra Moslemi, Sheethal Kumar, Ramesh Radhakrishnan,
• Abstract summary: AEMA plans, executes, and aggregates multistep evaluations across heterogeneous agentic under human oversight.<n>Compared to a single LLM-as-a-Judge, AEMA achieves greater stability, human alignment, and traceable records that support accountable automation.
• Score: 0.28055179094637683
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Evaluating large language model (LLM)-based multi-agent systems remains a critical challenge, as these systems must exhibit reliable coordination, transparent decision-making, and verifiable performance across evolving tasks. Existing evaluation approaches often limit themselves to single-response scoring or narrow benchmarks, which lack stability, extensibility, and automation when deployed in enterprise settings at multi-agent scale. We present AEMA (Adaptive Evaluation Multi-Agent), a process-aware and auditable framework that plans, executes, and aggregates multi-step evaluations across heterogeneous agentic workflows under human oversight. Compared to a single LLM-as-a-Judge, AEMA achieves greater stability, human alignment, and traceable records that support accountable automation. Our results on enterprise-style agent workflows simulated using realistic business scenarios demonstrate that AEMA provides a transparent and reproducible pathway toward responsible evaluation of LLM-based multi-agent systems. Keywords Agentic AI, Multi-Agent Systems, Trustworthy AI, Verifiable Evaluation, Human Oversight

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