Build a Cloud Resource Savings Planner
Build an agent that analyzes resource inventory, usage, cost, and ownership to propose infrastructure savings. Show the evidence, capacity impact, and review steps for every proposed change.
What you are building
The core problem, expected build, and operating context for this challenge.
A cloud operations team reviewing avoidable infrastructure spending.
Build an agent that analyzes resource inventory, usage, cost, and ownership to propose infrastructure savings. Show the evidence, capacity impact, and review steps for every proposed change.
Resource inventory
Usage and cost exports
Ownership and capacity policies
Join resource, cost, and usage records.
Identify idle or oversized resources.
Check ownership, recovery reservations, and capacity limits.
Produce a prioritized change plan with estimated savings.
A savings dashboard with resource-level evidence, exclusions, estimated monthly savings, and a change-plan export.
Recommend disk-A for review, preserve disk-B and node-C, and explain the supported monthly saving of 20.
A working local application with the specified review interface.
Source code, dependency lockfile, and setup commands.
A recorded end-to-end demo using the supplied starter data.
Example outputs, action logs, and automated tests.
These fictional records are a small development fixture, not a production dataset. Implement the mock services described in the brief.
```json { "request": "Find monthly savings without affecting recovery capacity.", "resources": [ { "id": "disk-A", "monthly_cost": 20, "attached": false, "owner": "ops", "restore_reserved": false }, { "id": "disk-B", "monthly_cost": 30, "attached": false, "owner": "ops", "restore_reserved": true }, { "id": "node-C", "monthly_cost": 100, "peak_utilization": 85, "minimum_headroom_percent": 15 } ] } ```
Read exported data and generate proposed changes only. Never apply infrastructure changes. Choose an available model that supports your implementation. Record its identifier and version.
Review the complete application, not only the starter-case answers.
Savings total is 20 for this bundle.
The recovery disk is excluded.
The plan contains no executed deletion. Add one normal case, one incomplete-input case, and one failure-recovery case.
Participation guide
Read exported data and generate proposed changes only. Never apply infrastructure changes.
How work is evaluated
Review workflow completion, interface usability, evidence quality, and safe failure handling. No agent execution has been verified.
A working local application with the specified review interface. Source code, dependency lockfile, and setup commands. A recorded end-to-end demo using the supplied starter data. Example outputs, action logs, and automated tests.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Join resource, cost, and usage records.
Identify idle or oversized resources.
Check ownership, recovery reservations, and capacity limits.
Produce a prioritized change plan with estimated savings.
Reference links and supporting material
Supplied fictional starter records: Resource inventory, Usage and cost exports, Ownership and capacity policies. The overview includes the complete JSON bundle. Implement local mock services and extend the sample coverage.
Challenge setup
Configuration summary for this challenge.
Challenge input
Input details are not configured.
Pydantic AI
Typed Python agent framework.
Evaluated output
No output checks are configured.
- No evaluation checks are configured.
- 1 public reference case
- 1 configured tool
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
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Configured tools
- Pydantic AI ยท Optional
Evaluation contract
- No evaluation checks are configured.
Reference cases
- 1 public reference case
Workflow tasks
No workflow tasks are configured.
Recipe freeze marker
No recipe freeze time is recorded.