Submitted:
16 December 2025
Posted:
17 December 2025
You are already at the latest version
Abstract
The rapid emergence of generative and agentic artificial intelligence (AI) has outpaced traditional evaluation practices. While large language models excel on static language benchmarks, real-world deployment demands more than accuracy on curated tasks. Agentic systems use planning, tool invocation, memory, and multi-agent collaboration to perform complex workflows. Enterprise adoption therefore hinges on holistic assessments that include cost, latency, reliability, safety, and multi-agent coordination. This survey provides a comprehensive taxonomy of evaluation dimensions, reviews existing benchmarks for generative and agentic systems, identifies gaps between laboratory tests and production requirements, and proposes future directions for more realistic, multi-dimensional benchmarking.
Keywords:
agentic AI
; generative AI
; AI evaluation and benchmarking
; autonomous intelligent systems
; tool-augmented language models
; multi-agent systems
; long-horizon planning
; memory and reasoning evaluation
; adaptive monitoring
; AI safety and robustness
; multimodal AI evaluation
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
