# FinOps Agent -- A Use-Case for IT Infrastructure and Cost Optimization

arXiv: https://arxiv.org/abs/2510.25914 · HTML: https://mlsanity.com/paper/2510.25914/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 29 Oct 2025.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 5 of 5)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [ABACUS: A FinOps Service for Cloud Cost Optimization](https://mlsanity.com/paper/2501.14753/) (2024)
- [Migrating to GraphQL: A Practical Assessment](https://mlsanity.com/paper/1906.07535/) (2019)
- [REST vs GraphQL: A Controlled Experiment](https://mlsanity.com/paper/2003.04761/) (2020)

## Built on by (most cited first, 1 of 1)

- [Towards Agentic Cloud Engineering: Graph and Loop Engineering with a Zero-Trust Agent Harness](https://mlsanity.com/paper/2609.00050/) (2026)

## Abstract (arXiv)

FinOps (Finance + Operations) represents an operational framework and cultural practice which maximizes cloud business value through collaborative financial accountability across engineering, finance, and business teams. FinOps practitioners face a fundamental challenge: billing data arrives in heterogeneous formats, taxonomies, and metrics from multiple cloud providers and internal systems which eventually lead to synthesizing actionable insights, and making time-sensitive decisions. To address this challenge, we propose leveraging autonomous, goal-driven AI agents for FinOps automation. In this paper, we built a FinOps agent for a typical use-case for IT infrastructure and cost optimization. We built a system simulating a realistic end-to-end industry process starting with retrieving data from various sources to consolidating and analyzing the data to generate recommendations for optimization. We defined a set of metrics to evaluate our agent using several open-source and close-source language models and it shows that the agent was able to understand, plan, and execute tasks as well as an actual FinOps practitioner.