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Challenge

Gemini 2.5 Pro & AutoGen for AI Hardware Optimization Agents

This challenge focuses on building an advanced multi-agent system designed to optimize AI model deployments for specific hardware architectures, such as AMD GPUs. The goal is to create an autonomous 'AI Operations' team that can analyze model requirements, profile hardware capabilities, propose optimal deployment configurations, and even generate code snippets for performance enhancements. Developers will use AutoGen to orchestrate a conversational multi-agent workflow, integrating Gemini 2.5 Pro for its powerful code generation, optimization, and deep technical reasoning. The system will feature MCP-enabled tool integration, allowing agents to interact with simulated enterprise hardware profiling APIs and cloud resource management systems, ensuring practical applicability for AI infrastructure management.

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Challenge brief

What you are building

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

Build an AutoGen multi-agent system with Gemini 2.5 Pro for optimizing AI model deployments on specific hardware, featuring MCP-enabled tool integration and deep reasoning.

Delivery guide

How work is evaluated

Evaluation

The system will be evaluated on its ability to generate optimized deployment configurations and code, demonstrate effective agent collaboration, and utilize MCP-enabled tools to achieve specific performance and cost targets for AI model inference on target hardware.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

Learning goals

What you should walk away with

  • Implement MCP-enabled tool integration to connect agents with simulated hardware profiling and cloud APIs.

  • Integrate Gemini 2.5 Pro for advanced code generation, performance analysis, and technical solutioning.

  • Design agents for deep reasoning, enabling complex problem-solving in hardware-software co-optimization.

  • Orchestrate a conversational multi-agent workflow using AutoGen for AI deployment optimization.

Resources and assets

Reference links and supporting material

Dataset notes

Sample data for 2 tasks

How this agent runs

The system will be evaluated on its ability to generate optimized deployment configurations and code, demonstrate effective agent collaboration, and utilize MCP-enabled tools to achieve specific performance and cost t...

Challenge input

{'ai_model_spec': {'name': 'string', 'parameters_count': 'int', 'inference_latency_target_ms': 'int'}, 'available_hardware_profiles': [{'id': 'stri...

Agent execution

The configured agent processes the input under the challenge policy.

Evaluated output

{'recommended_hardware_id': 'string', 'deployment_strategy': 'string', 'estimated_latency_ms': 'int', 'estimated_cost_per_hour_usd': 'float', 'opti...

Checks for
  • Recommended hardware and strategy must be technically plausible and meet latency targets (within a margin).
  • Generated code snippet must be syntactically correct for the specified language and contain relevant keywor...
  • Verify that agents successfully called MCP-enabled tools for hardware profiling and cost estimation (requir...
Proof of success
  • OptimizationScore target: 0.85
Runtime evidence
  • 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.

Frequently Asked Questions about Gemini 2.5 Pro & AutoGen for AI Hardware Optimization Agents