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Office Conversion Structural Feasibility Multi-Agent with AutoGen and Portia AI

Converting legacy office buildings into residential units hits severe architectural roadblocks such as floorplate depth and plumbing stack limits. Build a multi-agent architectural analysis system using AutoGen and Portia AI to evaluate floor plans and estimate conversion costs per square foot. Achieve less than 8% error in structural feasibility estimates.

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What you are building

Deploy AutoGen multi-agent workflows integrated with Portia AI automation to assess office-to-residential floorplate conversions.

How work is evaluated

Evaluation

Evaluates structural conversion cost estimation accuracy and feasibility classification.

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

feasibility_match

Checks correct classification of conversion feasibility.

Binary check

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

Dimension 2

cost_mape

Mean Absolute Percentage Error for estimated cost per square foot. • target: 0.08 • 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

  • Configure AutoGen multi-agent dialogue loops between Architectural and Structural engineering agents

  • Utilize Portia AI to execute automated plan analysis workflows and CAD metric extraction

  • Calculate key conversion feasibility indicators including window distance ratios and plumbing drop capacity

  • Produce detailed per-sqft cost estimates for adaptive reuse engineering

Resources

Dataset notes

Dataset of commercial office floorplate metrics, window ratios, riser counts, and historical retrofit actual costs.

Challenge setup

Evaluates structural conversion cost estimation accuracy and feasibility classification.

Challenge input

JSON object with floorplate_depth_ft, total_sqft, window_perimeter_ft, existing_risers

AutoGen

Assigned mandatory framework for multi-agent negotiation.

Portia AI

Assigned mandatory tool integration.

Evaluated output

JSON object with feasibility_status ('FEASIBLE'|'UNFEASIBLE'), est_cost_per_sqft

Evaluation checks
  • Checks correct classification of conversion feasibility.
  • Mean Absolute Percentage Error for estimated cost per square foot. • target: 0.08 • range: 0-1
Configured evaluation evidence
  • Benchmark: ConstructionRetrofitBench
  • Cost Mape target: 0.08
  • 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
  • Portia · Required
  • AutoGen · Optional
Observation
  • Helicone · Optional

Evaluation contract

  • feasibility_match · Weight 1
  • cost_mape · Weight 1

Metrics

  • cost_mape · Target 0.08

Benchmarks

  • ConstructionRetrofitBench
  • materials

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with floorplate_depth_ft, total_sqft, window_perimeter_ft, existing_risers

    Output: JSON object with feasibility_status ('FEASIBLE'|'UNFEASIBLE'), est_cost_per_sqft

    Execution harness: Python

Evaluation tools

  • AutoGen · Multi-agent Conversation Orchestrator
  • Portia AI · Workflow & Tool Automation Execution

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Office Conversion Structural Feasibility Multi-Agent with AutoGen and Portia AI