Engineering Intelligence CAD Analysis Pipeline with Claude Agents SDK and Google Jules
Static CAD designs lack automated thermal and stress validation, delaying manufacturing engineering reviews. Build an engineering intelligence agent using Claude Agents SDK and Google Jules to analyze STEP file metadata and automated FEA summaries. Ensure 90% accuracy in identifying manufacturability defects before physical prototyping.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an intelligent CAD inspection agent using Claude Agents SDK and Google Jules to evaluate manufacturing tolerances and design parameters.
How work is evaluated
Validate agent's capability to read parametric CAD metadata and accurately flag manufacturing risks and tolerance violations.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
defect_detection_pass
Detects all high-risk design flaws in benchmark dataset
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
dfm_accuracy
Accuracy of identified manufacturability issues • target: 0.9 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Build a file processing agent to extract geometric and material features from CAD/STEP export summaries
Utilize Claude Agents SDK with extended thinking to identify thermal and mechanical flaw risks
Integrate Google Jules AI engineering tools to generate actionable code fixes for parametric CAD files
Produce automated Design-for-Manufacturability (DFM) compliance reports
Reference links and supporting material
Parametric metadata summaries of 50 mechanical CAD parts including stress limits, wall thicknesses, fillet radii, and material specs.
Challenge setup
Validate agent's capability to read parametric CAD metadata and accurately flag manufacturing risks and tolerance violations.
Challenge input
JSON object describing wall thickness, material, and geometric constraints
Claude Agents SDK
Provides reasoning and execution workflow.
Google Jules
Automates structural refactoring scripts.
Evaluated output
JSON report listing detected_defects, risk_score, and recommended_design_change
- Detects all high-risk design flaws in benchmark dataset
- Accuracy of identified manufacturability issues • target: 0.9 • range: 0-1
- Benchmark: CAD Design for Manufacturability Benchmark
- Dfm Accuracy target: 0.9
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
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
- Google Jules · Required
- Google · Optional
- Claude Code · Optional
- Claude Code · Optional
- Google Jules · Required
Evaluation contract
- defect_detection_pass · Weight 1
- dfm_accuracy · Weight 1
Metrics
- dfm_accuracy · Target 0.9
Benchmarks
- CAD Design for Manufacturability Benchmark
- materials
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON object describing wall thickness, material, and geometric constraints
Output: JSON report listing detected_defects, risk_score, and recommended_design_change
Execution harness: Python
Evaluation tools
- Claude Agents SDK · Agent Orchestrator
- Google Jules · Engineering Automation Tool
Recipe freeze marker
No recipe freeze time is recorded.