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.
What you are building
The core problem, expected build, and operating context for this challenge.
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.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master CrewAI for defining roles, tasks, and collaboration dynamics within a multi-agent system, including hierarchical task decomposition.
Implement hybrid instant/deep reasoning by allowing OpenAI o3 agents to perform initial 'instant' checks, triggering Gemini 2.5 Pro's 'deep think' mode for detailed analysis when complexity warrants.
Build MCP-enabled tool wrappers and an MCP server mock-up to simulate secure, cross-enterprise system communication for agents to fetch design data, material properties, and cost parameters.
Develop a robust RAG pipeline using a vector database (e.g., ChromaDB, Milvus) to index manufacturing standards, material data sheets, and historical design failure patterns, accessible by agents.
Configure agents with specialized tools (e.g., a 'CAD Data Retriever' tool, a 'Standards Checker' tool) that use the MCP integration to query specific enterprise systems.
Design an output format for the final manufacturability report that includes identified issues, severity, suggested design changes, and a cost/time impact assessment.
How this agent runs
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
The configured agent processes the input under the challenge policy.
Evaluated output
{'design_id': 'string', 'potential_issues_found': bool, 'summary_report': 'string'}
- 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.
- IssueDetectionAccuracy target: 90
- 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.
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Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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