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Challenge

Multi-Cloud AI Compute Orchestration with A2A Protocol & GPT-5 Swarm

This challenge tasks developers with creating an advanced multi-agent system capable of dynamically orchestrating large-scale AI compute workloads across heterogeneous cloud environments (e.g., AWS and a simulated Azure/GCP endpoint). The system must optimize for cost, performance, and resource utilization, intelligently allocating tasks like model training, fine-tuning, and complex data processing. Participants will build an Agent-to-Agent (A2A) protocol-enabled 'swarm' of agents, using a pattern similar to AutoGen for collaborative execution. This 'swarm' will leverage GPT-5 for strategic planning and advanced code generation (e.g., infrastructure-as-code) and OpenAI o3 for efficient, high-throughput sub-tasks. The core focus is on secure, asynchronous cross-platform agent communication, intelligent resource management, and the implementation of adaptive reasoning budgets to minimize operational costs while maximizing computational output.

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Challenge brief

What you are building

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

Develop an A2A protocol-enabled multi-agent system leveraging GPT-5 and OpenAI o3 for orchestrating large-scale AI compute workloads across distributed cloud environments.

Delivery guide

How work is evaluated

Evaluation

Participants will submit a containerized (Docker) agent system designed to orchestrate a series of simulated AI compute workloads across a multi-cloud environment. Evaluation will focus on the system's ability to minimize cost, successfully complete tasks, and adhere to security and efficiency parameters.

Datasets

Challenge datasets

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

Learning goals

What you should walk away with

  • Implement adaptive reasoning budgets to dynamically optimize compute costs and performance for AI workloads across clouds.

  • Utilize GPT-5 for strategic planning, infrastructure-as-code generation, and complex problem-solving in a multi-cloud context.

  • Master A2A protocol for secure, asynchronous communication between agents across different cloud platforms.

  • Design and implement a 'swarm' of agents using an AutoGen-like pattern for collaborative multi-cloud compute orchestration.

Resources and assets

Reference links and supporting material

Dataset notes

Sample data for 2 tasks

Challenge setup

Participants will submit a containerized (Docker) agent system designed to orchestrate a series of simulated AI compute workloads across a multi-cloud environment. Evaluation will focus on the system's ability to mini...

Challenge input

{ 'scenario_id': 'string', 'model_size_gb': int, 'dataset_size_gb': int, 'required_gpu_hours': int, 'deadline_hours': int, 'max_budget_usd': float }

Agent execution

No agent tool is listed for this challenge.

Evaluated output

{ 'cloud_allocations': [{'cloud': 'AWS'|'Azure', 'instance_type': 'string', 'region': 'string', 'gpu_hours': int, 'cost_usd': float}], 'total_cost_...

Evaluation checks
  • The primary compute task (training or processing) is reported as completed.
  • The agent_communication_log indicates active, multi-agent communication via A2A protocol (simulated).
  • For high-security tasks, security audit report indicates proper measures.
Configured evaluation evidence
  • CostEfficiency target: 0.85
Configured runtime
  • Docker 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.

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Configured tools

No tool records are attached.

Evaluation contract

  • The primary compute task (training or processing) is reported as completed.
  • The agent_communication_log indicates active, multi-agent communication via A2A protocol (simulated).
  • For high-security tasks, security audit report indicates proper measures.

Metrics

  • CostEfficiency · Target 0.85
  • Performance (Time to Completion) · Target 0.9
  • ResourceUtilization · Target 0.8
  • GPT-5 Planning Quality · Target 0.75

Workflow tasks

  1. Task 1

    Input: { 'scenario_id': 'string', 'model_size_gb': int, 'dataset_size_gb': int, 'required_gpu_hours': int, 'deadline_hours': int, 'max_budget_usd': float }

    Output: { 'cloud_allocations': [{'cloud': 'AWS'|'Azure', 'instance_type': 'string', 'region': 'string', 'gpu_hours': int, 'cost_usd': float}], 'total_cost_usd': float, 'completion_time_hours': float, 'explanation': 'string', 'agent_communication_log': [] }

    Execution harness: Docker

  2. Task 2

    Input: { 'scenario_id': 'string', 'data_location_aws': 's3://bucket/path', 'data_location_azure': 'https://storageaccount.blob.core.windows.net/container/path', 'processing_task_description': 'string', 'security_level': 'high'|'medium' }

    Output: { 'processing_status': 'completed'|'failed', 'result_location_aws': 's3://bucket/output', 'processing_logs_summary': 'string', 'security_audit_report': 'string', 'agent_communication_log': [] }

    Execution harness: Docker

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Multi-Cloud AI Compute Orchestration with A2A Protocol & GPT-5 Swarm