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Challenge

Cloud Infrastructure Cost Triage Agent with Google ADK and Mastra AI

Managing high GPU infrastructure costs during cloud expansion presents strict fiscal and operational challenges for data platforms. Build an intelligent cost triage agent using Google ADK with Gemini and Mastra AI workflow nodes to identify underutilized compute resources, analyze pricing paradoxes, and automate dynamic instance right-sizing.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Construct a Google ADK agent integrated with Mastra AI tools to monitor sovereign cloud GPU usage metrics and issue automated right-sizing recommendations.

Delivery guide

How work is evaluated

Evaluation

Evaluates Google ADK agent recommendations against benchmark cloud telemetry workloads.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

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

correct_underutilized_identification

Verifies that instances with <10% utilization are correctly targeted.

Binary check

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

Dimension 2

cost_saving_efficiency

Ratio of accurately identified savings vs total potential waste. • target: 0.95 • 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

What you should walk away with

  • Use Google Agent Development Kit (ADK) to build Gemini-powered infrastructure optimization agents.

  • Integrate Mastra AI workflow constructs for orchestration across heterogeneous cloud telemetry.

  • Analyze GPU utilization metrics to resolve pricing paradox issues in public cloud DPI setups.

Resources and assets

Reference links and supporting material

Dataset notes

Telemetry logs from cloud infrastructure containing node GPU/CPU memory utilization, hourly pricing, and active container assignments.

How this agent runs

Evaluates Google ADK agent recommendations against benchmark cloud telemetry workloads.

Challenge input

JSON containing cluster telemetry, hourly costs, and utilization percentage.

Google ADK

Native agent framework for Gemini integration

Mastra AI

Modular agent step orchestrator

Evaluated output

JSON containing action_plan, estimated_cost_reduction_pct, and target_instances.

Checks for
  • Verifies that instances with <10% utilization are correctly targeted.
  • Ratio of accurately identified savings vs total potential waste. • target: 0.95 • range: 0-1
Proof of success
  • Benchmark: Cloud FinOps Agent Benchmark
  • Cost Saving Efficiency target: 0.95
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • Google ADK · Required
  • Mastra AI · Optional
  • Google · Optional
Orchestration
  • Mastra AI · Optional

Evaluation contract

  • correct_underutilized_identification · Weight 1
  • cost_saving_efficiency · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

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