# Agentic AI Workload Characteristics

arXiv: https://arxiv.org/abs/2605.26297 · HTML: https://mlsanity.com/paper/2605.26297/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2026.

## Built on by (most cited first, 4 of 4)

- [From LLM Inference to Agentic Workloads: Characterization and Implications for Serving Systems](https://mlsanity.com/paper/2608.15127/) (2026)
- [Architectural Implications of Agentic AI Workflows](https://mlsanity.com/paper/2608.04458/) (2026)
- [WhiteMatter: All-to-All Cross-Layer Connections via KV Source Mixing](https://mlsanity.com/paper/2608.18486/) (2026)
- [SPLASH: Switching Parallel Layouts of Attention with Seamless Handoff for LLM Serving](https://mlsanity.com/paper/2609.37626/) (2026)

## Abstract (arXiv)

Agentic AI shifts LLM serving from isolated prompt-generation requests to stateful, multi-turn executions that repeatedly invoke the model, call tools, and grow context over time. This paper characterizes ReAct-style agents from both the LLM-serving and tool-execution perspectives using an end-to-end tracing infrastructure across reasoning and non-reasoning Gemma and Qwen configurations on five agentic benchmarks. Our study shows that agentic workloads are not simply long-prompt workloads: with effective context caching, most input tokens are reused across turns, making execution decode-dominated while increasing dependence on long-lived KV-cache state. We also find that tool use has a clear temporal structure, with agents shifting from read/explore behavior early in execution to execute/write behavior later. These results show that efficient agentic serving must jointly manage repeated model re-entry, persistent context state, and workload-dependent tool behavior.