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The Necessity of a Unified Framework for LLM-Based Agent Evaluation

Pengyu Zhu, Li Sun, Philip S. Yu, Sen Su

TL;DR

This paper argues that evaluating LLM-based agents cannot rely on static benchmarks due to confounding factors from system prompts, tool interfaces, and dynamic environments. It advocates a unified framework centered on a hermetic Sandbox and a principled Evaluation Methodology to ensure reproducible, fair assessments of agentic capability. The authors decompose evaluation variance into inference, prompting, memory, tool invocation, and external environments, and provide a concrete feasibility proposal featuring a standardized Instruction/Tool/Environment dataset, a unified agent architecture, and a multidimensional evaluation protocol. If adopted broadly, this framework could enable more reliable attribution of performance to genuine agentic improvements and accelerate rigorous progress in the field.

Abstract

With the advent of Large Language Models (LLMs), general-purpose agents have seen fundamental advancements. However, evaluating these agents presents unique challenges that distinguish them from static QA benchmarks. We observe that current agent benchmarks are heavily confounded by extraneous factors, including system prompts, toolset configurations, and environmental dynamics. Existing evaluations often rely on fragmented, researcher-specific frameworks where the prompt engineering for reasoning and tool usage varies significantly, making it difficult to attribute performance gains to the model itself. Additionally, the lack of standardized environmental data leads to untraceable errors and non-reproducible results. This lack of standardization introduces substantial unfairness and opacity into the field. We propose that a unified evaluation framework is essential for the rigorous advancement of agent evaluation. To this end, we introduce a proposal aimed at standardizing agent evaluation.

The Necessity of a Unified Framework for LLM-Based Agent Evaluation

TL;DR

This paper argues that evaluating LLM-based agents cannot rely on static benchmarks due to confounding factors from system prompts, tool interfaces, and dynamic environments. It advocates a unified framework centered on a hermetic Sandbox and a principled Evaluation Methodology to ensure reproducible, fair assessments of agentic capability. The authors decompose evaluation variance into inference, prompting, memory, tool invocation, and external environments, and provide a concrete feasibility proposal featuring a standardized Instruction/Tool/Environment dataset, a unified agent architecture, and a multidimensional evaluation protocol. If adopted broadly, this framework could enable more reliable attribution of performance to genuine agentic improvements and accelerate rigorous progress in the field.

Abstract

With the advent of Large Language Models (LLMs), general-purpose agents have seen fundamental advancements. However, evaluating these agents presents unique challenges that distinguish them from static QA benchmarks. We observe that current agent benchmarks are heavily confounded by extraneous factors, including system prompts, toolset configurations, and environmental dynamics. Existing evaluations often rely on fragmented, researcher-specific frameworks where the prompt engineering for reasoning and tool usage varies significantly, making it difficult to attribute performance gains to the model itself. Additionally, the lack of standardized environmental data leads to untraceable errors and non-reproducible results. This lack of standardization introduces substantial unfairness and opacity into the field. We propose that a unified evaluation framework is essential for the rigorous advancement of agent evaluation. To this end, we introduce a proposal aimed at standardizing agent evaluation.
Paper Structure (29 sections, 2 figures)

This paper contains 29 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Background on Agent Evaluation
  3. From LLM Evaluation to Agent Evaluation
  4. Core Components and Instantiation of Agents
  5. Agent Benchmarks and Evaluation
  6. Sources of Variance in Evaluation Frameworks
  7. Inference Configuration
  8. Inference Interfaces and Protocols.
  9. Inference Parameters and Stochasticity.
  10. Inference Engine Variability.
  11. Prompting and Planning Strategies
  12. Prompting.
  13. Planning Strategies.
  14. Memory Mechanisms
  15. Memory Formatting.
  16. ...and 14 more sections

Figures (2)

  • Figure 1: Overview of LLM-based agent evaluation, where correctness is assessed over trajectories, final answer, and environment state changes rather than a single output.
  • Figure 2: A unified framework where the sandbox integrates a standardized dataset and a unified agent architecture, coupled with multidimensional evaluation.