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Challenge

Cloud GPU Pricing Triage Agent with LangChain, LangGraph & AGiXT

Sovereign cloud providers struggle with volatile GPU pricing and dynamic compute allocation for public AI services. Build an agentic cloud cost optimizer using LangChain and AGiXT to monitor GPU cluster utilization and lower idle infrastructure spend by 25%.

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Challenge brief

What you are building

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

Build an intelligent sovereign cloud GPU dynamic pricing and allocation agent using LangChain, LangGraph, and AGiXT.

Delivery guide

How work is evaluated

Evaluation

Evaluates the agent's dynamic GPU reallocation recommendations based on synthetic cluster metrics.

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

check_savings_threshold

Checks if proposed optimization achieves minimum 20% cost reduction

Binary check

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

Dimension 2

cost_efficiency_score

Ratio of cost reduction achieved vs theoretical maximum without SLA breach • target: 0.88 • 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

  • Design stateful multi-step optimization graphs using LangGraph

  • Integrate AGiXT agent management for continuous multi-model monitoring

  • Build dynamic spot price vs. reserved instance trade-off algorithms

  • Implement real-time telemetry parsers for GPU cluster utilization

Resources and assets

Reference links and supporting material

Dataset notes

Telemetry dataset of GPU datacenter load profiles, thermal stats, spot pricing curves, and batch job priority queues.

Challenge setup

Evaluates the agent's dynamic GPU reallocation recommendations based on synthetic cluster metrics.

Challenge input

JSON with cluster_utilization, node_costs, and pending_jobs

LangChain

Comprehensive framework for LLM-powered chains, memory, and agents.

AGiXT

Dynamic agent management and orchestration system.

Evaluated output

JSON containing migration_plan, projected_savings_pct, and status

Evaluation checks
  • Checks if proposed optimization achieves minimum 20% cost reduction
  • Ratio of cost reduction achieved vs theoretical maximum without SLA breach • target: 0.88 • range: 0-1
Configured evaluation evidence
  • Benchmark: Cloud Infrastructure Cost Efficiency Index
  • Cost Efficiency Score target: 0.88
  • 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
  • AGiXT · Optional
  • Resemble AI · Optional
Orchestration
  • Langchain · Required

Evaluation contract

  • check_savings_threshold · Weight 1
  • cost_efficiency_score · Weight 1

Metrics

  • cost_efficiency_score · Target 0.88

Benchmarks

  • Cloud Infrastructure Cost Efficiency Index
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON with cluster_utilization, node_costs, and pending_jobs

    Output: JSON containing migration_plan, projected_savings_pct, and status

    Execution harness: Python

Evaluation tools

  • LangChain · Agent Orchestration Framework
  • AGiXT · Agent Systems

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Cloud GPU Pricing Triage Agent with LangChain, LangGraph & AGiXT