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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.

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What you are building

Build an intelligent CAD inspection agent using Claude Agents SDK and Google Jules to evaluate manufacturing tolerances and design parameters.

How work is evaluated

Evaluation

Validate agent's capability to read parametric CAD metadata and accurately flag manufacturing risks and tolerance violations.

Datasets

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

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

defect_detection_pass

Detects all high-risk design flaws in benchmark dataset

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

dfm_accuracy

Accuracy of identified manufacturability issues • target: 0.9 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • 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

Resources

Dataset notes

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

Evaluation checks
  • Detects all high-risk design flaws in benchmark dataset
  • Accuracy of identified manufacturability issues • target: 0.9 • range: 0-1
Configured evaluation evidence
  • Benchmark: CAD Design for Manufacturability Benchmark
  • Dfm Accuracy target: 0.9
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Google Jules · Required
  • Google · Optional
  • Claude Code · Optional
Observation
  • Claude Code · Optional
Orchestration
  • 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

  1. 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.

Frequently Asked Questions about Engineering Intelligence CAD Analysis Pipeline with Claude Agents SDK and Google Jules