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Build an Infrastructure FinOps Cost Optimization Agent with CrewAI

Construct a multi-agent AI server cost optimization system to manage hardware price surge pressures. Powered by CrewAI, role-playing agents (Procurement Analyst, FinOps Engineer, Capacity Planner) will audit server cluster usage, evaluate memory price hikes, and re-allocate workloads efficiently.

Workflow AutomationHosted by Vera
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What you are building

Orchestrate CrewAI agents to audit server hardware cost inflation and optimize enterprise AI cloud capacity allocation.

How work is evaluated

Evaluation

Evaluate FinOps cost reduction proposal and verify calculation of server cost increases.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

math_correctness

TCO increase matches mathematical percentage surge exactly

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

recommendation_relevance

Quality score of FinOps savings action • target: 0.9 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • Define CrewAI agents tailored for enterprise hardware cost governance

  • Build automated calculators for server price hikes driven by memory component costs

  • Formulate workload consolidation plans to absorb hardware inflation

  • Output structured cloud FinOps procurement recommendations

Resources

Dataset notes

Data center compute node bill-of-materials and cloud instance pricing tables.

Challenge setup

Evaluate FinOps cost reduction proposal and verify calculation of server cost increases.

Challenge input

JSON with server_count, unit_cost, memory_surge_pct

CrewAI

Framework for multi-agent collaborative task teams.

Evaluated output

JSON with new_total_tco, cost_increase_usd, recommended_action

Evaluation checks
  • TCO increase matches mathematical percentage surge exactly
  • Quality score of FinOps savings action • target: 0.9 • range: 0-1
Configured evaluation evidence
  • Benchmark: Cloud_FinOps_Bench
  • Recommendation Relevance target: 0.9
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • CrewAI · Required
  • crewAI · Optional
  • Capably · Optional

Evaluation contract

  • math_correctness · Weight 1
  • recommendation_relevance · Weight 1

Metrics

  • recommendation_relevance · Target 0.9

Benchmarks

  • Cloud_FinOps_Bench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON with server_count, unit_cost, memory_surge_pct

    Output: JSON with new_total_tco, cost_increase_usd, recommended_action

    Execution harness: Python

Evaluation tools

  • CrewAI · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Build an Infrastructure FinOps Cost Optimization Agent with CrewAI