Standardizing Evaluation Toolchains and Stability Reporting

Develop standardized evaluation toolchains for LLM-based AI agents that mandate reporting of cost and latency and that quantify stability across runs to improve comparability and reproducibility under realistic environment and tool variability.

Background

Agent outcomes are highly sensitive to prompts, sampling, tools, and environment drift. While benchmarks have improved comparability, differences in toolchains and reporting practices hinder fair assessment.

Standardization should include cost/latency metrics, multi-seed stability, versioning of tools and environments, and trace completeness to reflect deployable reliability rather than single-run best cases.

References

Benchmarks such as WebArena, SWE-bench, ToolBench, and AgentBench have improved comparability, but open problems remain in standardizing toolchains, reporting cost/latency, and measuring stability across runs.

— AI Agent Systems: Architectures, Applications, and Evaluation  (2601.01743 - Xu, 5 Jan 2026) in Section 7.4 (Robust Evaluation and Reproducibility Under Realistic Variability)