FinOps Agent -- A Use-Case for IT Infrastructure and Cost Optimization
This addresses cost optimization for FinOps teams, but it is incremental as it applies existing AI agent methods to a new domain.
The paper tackled the challenge of heterogeneous billing data in FinOps by building an autonomous AI agent for IT infrastructure and cost optimization, showing it performed as well as a human practitioner in understanding, planning, and executing tasks.
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.