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Challenge

LangChain Sovereign Cloud GPU Capacity Optimization Agent

Cloud providers face extreme GPU pricing volatility and high sovereign infrastructure allocation constraints. You will build a LangGraph stateful agent that balances AI workload demands against localized GPU power and sovereign cloud node pricing schemas.

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Challenge brief

What you are building

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

Orchestrate a stateful LangChain/LangGraph agent that monitors GPU cluster usage and dynamically reroutes batch inference workloads across sovereign data centers.

Delivery guide

How work is evaluated

Evaluation

Evaluates the agent's workload placement decisions under strict pricing and data localization constraints.

Datasets

Challenge datasets

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

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

Sovereign Compliance Check

HIGH_SOVEREIGN data must remain strictly inside in-country nodes

Binary check

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

Dimension 2

Cost Efficiency Ratio

Savings relative to max target budget • target: 0.95 • range: 0.8-1

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

  • Construct a stateful LangGraph state machine with conditional routing edges

  • Integrate LangChain tool wrappers for real-time GPU cluster telemetry APIs

  • Implement sovereign data residency constraints into dynamic load balance decisions

  • Optimize cost per compute job across GPU instances under fluctuating spot prices

Resources and assets

Reference links and supporting material

Dataset notes

Telemetry feed of GPU node usage, spot price fluctuations, and sovereign cloud data residency policies.

Challenge setup

Evaluates the agent's workload placement decisions under strict pricing and data localization constraints.

Challenge input

JSON with job_type, required_vram_gb, data_sensitivity, and target_budget

LangChain

Provides stateful agent orchestration with LangGraph.

Evaluated output

JSON with allocated_region, node_type, estimated_cost_hr, and compliance_flag

Evaluation checks
  • HIGH_SOVEREIGN data must remain strictly inside in-country nodes
  • Savings relative to max target budget • target: 0.95 • range: 0.8-1
Configured evaluation evidence
  • Benchmark: CloudOps-Bench
  • Cost Efficiency Ratio target: 0.95
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Langchain · Required
  • LangChain · Optional
  • Zed · Optional
Orchestration
  • Langchain · Required
  • LangChain · Optional

Evaluation contract

  • Sovereign Compliance Check · Weight 1
  • Cost Efficiency Ratio · Weight 1

Metrics

  • Cost Efficiency Ratio · Target 0.95

Benchmarks

  • CloudOps-Bench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON with job_type, required_vram_gb, data_sensitivity, and target_budget

    Output: JSON with allocated_region, node_type, estimated_cost_hr, and compliance_flag

    Execution harness: Python

Evaluation tools

  • LangChain · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about LangChain Sovereign Cloud GPU Capacity Optimization Agent