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Challenge

Adaptive Multi-Cloud AI Resource Optimizer

Develop an advanced agentic system using LangGraph that acts as an intelligent optimizer for AI workloads across a simulated multi-cloud environment. The system will dynamically adjust compute resources, leveraging GPT-5 for complex strategic decision-making and OpenAI o3 for real-time monitoring and anomaly detection. It will employ adaptive reasoning budgets to optimize cost and performance while considering environmental impact, with secure tool integration via MCP for cloud provider APIs.

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Challenge brief

What you are building

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

Build a graph-based multi-agent system using LangGraph and MCP for adaptive resource optimization in a simulated multi-cloud AI environment with GPT-5 and OpenAI o3.

Delivery guide

How work is evaluated

Evaluation

The solution will be evaluated on its ability to propose and execute optimal resource adjustments in a simulated multi-cloud environment, demonstrating adaptive reasoning, efficient use of agent workflows, and adherence to cost/performance targets.

Datasets

Challenge datasets

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

Learning goals

What you should walk away with

  • Orchestrate hybrid reasoning using GPT-5 and OpenAI o3 for complex resource allocation and real-time monitoring.

  • Integrate the A2A Protocol for secure, cross-agent communication within the LangGraph structure.

  • Design and implement graph-based agent workflows with LangGraph for dynamic decision-making.

  • Develop and apply adaptive reasoning budgets for cost-effective, context-aware decision-making using GPT-5.

  • Implement MCP for standardized and secure tool interaction with mock cloud provider APIs.

Resources and assets

Reference links and supporting material

Dataset notes

Sample data for 1 tasks

Challenge setup

The solution will be evaluated on its ability to propose and execute optimal resource adjustments in a simulated multi-cloud environment, demonstrating adaptive reasoning, efficient use of agent workflows, and adheren...

Challenge input

{ "current_config": "object", "cost_target": "number", "performance_target": "number", "simulated_cloud_metrics": "object" }

Agent execution

No agent tool is listed for this challenge.

Evaluated output

{ "proposed_config": "object", "justification": "string", "predicted_cost": "number", "predicted_performance": "number" }

Evaluation checks
  • Verifies that the LangGraph workflow executes without errors, transitions between states correctly, and han...
  • Checks if simulated cloud APIs are correctly called and interacted with via the MCP framework for monitorin...
  • Confirms the agent system can dynamically adjust reasoning depth (e.g., switching between GPT-5 budget leve...
Configured evaluation evidence
  • CostOptimizationRatio target: 0.95
Configured runtime
  • Python execution harness

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

No tool records are attached.

Evaluation contract

  • Verifies that the LangGraph workflow executes without errors, transitions between states correctly, and handles conditional logic as designed.
  • Checks if simulated cloud APIs are correctly called and interacted with via the MCP framework for monitoring and action execution.
  • Confirms the agent system can dynamically adjust reasoning depth (e.g., switching between GPT-5 budget levels or models) based on defined criteria.

Metrics

  • CostOptimizationRatio · Target 0.95
  • PerformanceAdherence · Target 0.98
  • DecisionJustificationQuality · Target 0.9

Workflow tasks

  1. Task 1

    Input: { "current_config": "object", "cost_target": "number", "performance_target": "number", "simulated_cloud_metrics": "object" }

    Output: { "proposed_config": "object", "justification": "string", "predicted_cost": "number", "predicted_performance": "number" }

    Execution harness: Python

Recipe freeze marker

No recipe freeze time is recorded.

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