CrewAI Office-to-Residential Structural Conversion Evaluator
Architect a multi-agent system with CrewAI and Cognition Devin that analyzes structural drawings and floor depths for office-to-residential conversions. The system evaluates MEP core placements and produces actionable conversion feasibility metrics with 90% confidence.
What you are building
The core problem, expected build, and operating context for this challenge.
Utilizes CrewAI multi-agent orchestration alongside Cognition Devin dynamic coding agents to process CAD metadata and structural office plans for residential adaptive reuse.
How work is evaluated
Evaluates CrewAI crew performance on structural feasibility analysis.
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.
Structural Constraint Logic Check
Validates that high depth/post-tensioned slab triggers plumbing risk flags
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Feasibility Score Variance
Variance against expert structural engineering feasibility baseline • target: 2 • range: 0-10
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Orchestrate collaborative CrewAI agents with dedicated architectural and MEP engineering roles
Integrate Cognition Devin workflows for automated CAD layout code parsing
Evaluate window perimeter ratios and floorplate core depth constraints
Generate cost-per-square-foot adaptive reuse scorecards
Reference links and supporting material
Dataset of 30 commercial office floorplate plans with corresponding structural specs and residential conversion outcomes.
Challenge setup
Evaluates CrewAI crew performance on structural feasibility analysis.
Challenge input
JSON containing floor_depth_ft, core_to_window_distance_ft, slab_type, and column_grid_spacing
CrewAI
Orchestrates multi-role specialized engineering agents.
Cognition Devin
Executes CAD parser scripts in an automated sandbox.
Evaluated output
JSON with conversion_score (0-100), plumbing_chase_violation (bool), and structural_notes
- Validates that high depth/post-tensioned slab triggers plumbing risk flags
- Variance against expert structural engineering feasibility baseline • target: 2 • range: 0-10
- Benchmark: StructuralFeasibilityBench-CRE
- Feasibility Score Variance target: 2
- 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.
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Configured tools
- CrewAI · Required
- crewAI · Optional
- Sacred · Optional
Evaluation contract
- Structural Constraint Logic Check · Weight 1
- Feasibility Score Variance · Weight 1
Metrics
- Feasibility Score Variance · Target 2
Benchmarks
- StructuralFeasibilityBench-CRE
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing floor_depth_ft, core_to_window_distance_ft, slab_type, and column_grid_spacing
Output: JSON with conversion_score (0-100), plumbing_chase_violation (bool), and structural_notes
Execution harness: Python
Evaluation tools
- CrewAI · Agent Framework
- Cognition Devin · Code Engine
Recipe freeze marker
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