Skip to main content
Back to challenges
Challenge

Hybrid Reasoning for Hardware Manufacturability Analysis

This challenge involves building a sophisticated multi-agent system using CrewAI to automate manufacturability analysis for hardware designs. The system will leverage a team of specialized agents, each powered by either Gemini 2.5 Pro (for deep engineering reasoning) or OpenAI o3 (for concise summarization and interface). Agents will employ a hybrid instant/deep reasoning approach, quickly assessing design concepts and then diving into detailed analysis when manufacturability issues are detected or specific constraints require complex evaluation. A critical component will be MCP-enabled tool integration, allowing agents to connect with simulated enterprise systems like CAD (for design geometry), PDM (for material data), and ERP (for cost and supply chain constraints). The agents will use advanced RAG techniques to query design specifications and manufacturing standards, providing comprehensive feedback to engineers. The goal is to identify potential manufacturing challenges early in the design cycle, reducing costs and accelerating development.

Workflow AutomationHosted by Vera
Sign in free to start
Challenge brief

What you are building

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

Orchestrate a CrewAI multi-agent system with Gemini 2.5 Pro and OpenAI o3 using hybrid reasoning and MCP-enabled tool integration for automated hardware manufacturability analysis.

Delivery guide

How work is evaluated

Evaluation

The evaluation will assess the CrewAI system's ability to accurately identify manufacturability issues, the effectiveness of its hybrid reasoning, the proper integration of MCP-enabled tools, and the quality of the final design feedback report. Emphasis will be on precision in constraint checking and practicality of recommendations.

Datasets

Challenge datasets

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

Learning goals

What you should walk away with

  • Implement a hybrid instant/deep reasoning architecture with Gemini 2.5 Pro for nuanced manufacturability analysis.

  • Design and integrate MCP-enabled tools for seamless interaction with simulated CAD, PDM, and ERP systems.

  • Generate structured reports identifying manufacturability risks, proposing design adjustments, and estimating cost implications.

  • Develop advanced RAG capabilities to query and synthesize information from detailed design specifications and manufacturing standards.

  • Orchestrate role-based agent teams using CrewAI for collaborative problem-solving in complex engineering domains.

Resources and assets

Reference links and supporting material

Dataset notes

Sample data for 2 tasks

Challenge setup

The evaluation will assess the CrewAI system's ability to accurately identify manufacturability issues, the effectiveness of its hybrid reasoning, the proper integration of MCP-enabled tools, and the quality of the fi...

Challenge input

{'design_id': 'string', 'high_level_specs': {'material': 'string', 'geometry_type': 'string', 'volume': float}}

Agent execution

No agent tool is listed for this challenge.

Evaluated output

{'design_id': 'string', 'potential_issues_found': bool, 'summary_report': 'string'}

Evaluation checks
  • Ensure distinct agent roles and a collaborative flow are defined within CrewAI.
  • Verify that agents successfully interact with simulated enterprise system APIs via the MCP layer.
  • Confirm that deep reasoning (Gemini 2.5 Pro) is triggered appropriately based on complexity or specific flags.
Configured evaluation evidence
  • IssueDetectionAccuracy target: 90
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

  • Ensure distinct agent roles and a collaborative flow are defined within CrewAI.
  • Verify that agents successfully interact with simulated enterprise system APIs via the MCP layer.
  • Confirm that deep reasoning (Gemini 2.5 Pro) is triggered appropriately based on complexity or specific flags.

Metrics

  • IssueDetectionAccuracy · Target 90
  • RecommendationQuality · Target 4
  • RAGEffectiveness · Target 4

Workflow tasks

  1. Task 1

    Input: {'design_id': 'string', 'high_level_specs': {'material': 'string', 'geometry_type': 'string', 'volume': float}}

    Output: {'design_id': 'string', 'potential_issues_found': bool, 'summary_report': 'string'}

    Execution harness: Python

  2. Task 2

    Input: {'design_id': 'string', 'full_design_data': {'cad_model_link': 'string', 'pdm_data': {'material_properties': {}, 'tolerances': {}}, 'erp_data': {'cost_targets': float, 'supply_chain_constraints': []}}}

    Output: {'design_id': 'string', 'status': 'pass|fail|review', 'detailed_issues': [{'issue_id': 'string', 'description': 'string', 'severity': 'low|medium|high', 'suggested_action': 'string', 'impact': {'cost': float, 'time': float}}]}

    Execution harness: Python

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Hybrid Reasoning for Hardware Manufacturability Analysis