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.
What you are building
The core problem, expected build, and operating context for this challenge.
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.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master LangGraph for defining stateful, reactive agent workflows, enabling dynamic graph modifications, conditional routing, and persistent state management.
Implement the A2A Protocol for secure, verifiable, and structured agent-to-agent communication within the LangGraph structure, ensuring seamless state sharing and collaborative decision-making.
Design and deploy MCP-enabled agents for seamless and secure tool integration with mock cloud provider APIs (e.g., for scaling instances, querying cost data, fetching power consumption metrics from AWS, Azure, GCP).
Build extended thinking modules using GPT-5 (or GPT-5 Pro if available) for deep analysis of complex factors like cost-performance trade-offs, simulated geopolitical influences, and environmental impact data.
Implement adaptive reasoning budgets: dynamically adjust the complexity and computational resources allocated to GPT-5 reasoning based on real-time cost constraints, urgency of tasks, or monitoring feedback (e.g., using OpenAI o3 for quick checks and GPT-5 for deeper analysis).
Develop specialized agents within the LangGraph workflow: a 'Monitoring Agent' (leveraging OpenAI o3 for real-time data ingestion and anomaly detection), an 'Optimizer Agent' (utilizing GPT-5 for strategic resource allocation decisions), and a 'Deployment Agent' (MCP-enabled for executing cloud actions).
Orchestrate a dynamic decision-making process where agents analyze current resource usage, forecast future needs, propose optimal resource adjustments across different simulated cloud providers, and justify their recommendations based on predefined objectives.
How this agent runs
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
The configured agent processes the input under the challenge policy.
Evaluated output
{ "proposed_config": "object", "justification": "string", "predicted_cost": "number", "predicted_performance": "number" }
- 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...
- CostOptimizationRatio target: 0.95
- Python execution harness
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- The evaluation module defines the checks.
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
Run this agent on your dataset and AI stack
Bring your dataset, model providers, and success criteria. We will scope the right managed run for your team.
Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
Requires VERSALIST_API_KEY. Works with any MCP-aware editor.
DocsFind another challenge
Jump to a random challenge when you want a fresh benchmark or a different problem space.