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

TL;DR

AEMA is a comprehensive framework designed to evaluate multi-agent LLM systems by providing transparent, stable, and human-aligned assessments that support responsible automation in enterprise scenarios.

Contribution

It introduces a process-aware, auditable evaluation framework that enhances stability, transparency, and automation for multi-agent LLM systems compared to traditional single-response methods.

Findings

AEMA improves evaluation stability and human alignment.

It offers traceable records for accountable automation.

Demonstrates effectiveness in realistic enterprise scenarios.

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