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Challenge

CrewAI & Daytona Multi-Agent Capital Allocation Engine

Major real estate developers restarting development engines need cross-departmental coordination across underwriting, risk, and design. Build a CrewAI multi-agent team inside isolated Daytona sandbox environments to evaluate mixed-use parcel development potential.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Orchestrate role-playing CrewAI agents executed inside Daytona environments to perform end-to-end development underwriting.

Delivery guide

How work is evaluated

Evaluation

Evaluates multi-agent investment memo synthesis and financial metric calculations.

Datasets

Challenge datasets

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

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

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

irr_threshold_check

Ensures projected IRR matches mathematical discount rate models within 2%.

Binary check

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

Dimension 2

memo_completeness_score

Score measuring presence of market, engineering, and financial sub-sections. • target: 0.95 • 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

What you should walk away with

  • Create specialized CrewAI agents for Market Analyst, Structural Engineer, and Underwriter

  • Configure Daytona workspace environments for secure code and model execution

  • Implement collaborative agent tasks using CrewAI sequential and hierarchical workflows

  • Generate comprehensive real estate pro forma financial reports automatically

Resources and assets

Reference links and supporting material

Dataset notes

100 commercial development parcel proposals including zoning codes, cost benchmarks, and market rent metrics.

Challenge setup

Evaluates multi-agent investment memo synthesis and financial metric calculations.

Challenge input

JSON parcel characteristics and construction cost assumptions

CrewAI

Provides multi-agent task delegation and role-playing capabilities.

Daytona

Provides isolated, standard environments to execute agent code and tools.

Evaluated output

JSON containing estimated_cost, projected_irr, recommendation

Evaluation checks
  • Ensures projected IRR matches mathematical discount rate models within 2%.
  • Score measuring presence of market, engineering, and financial sub-sections. • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Benchmark: CREUnderwritingBench
  • Memo Completeness Score target: 0.95
  • 1 public reference case
Configured runtime
  • 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

Environment
  • Daytona · Optional
Action Space
  • CrewAI · Required
  • Daytona · Optional
  • Aim · Optional

Evaluation contract

  • irr_threshold_check · Weight 1
  • memo_completeness_score · Weight 1

Metrics

  • memo_completeness_score · Target 0.95

Benchmarks

  • CREUnderwritingBench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON parcel characteristics and construction cost assumptions

    Output: JSON containing estimated_cost, projected_irr, recommendation

    Execution harness: Python

Evaluation tools

  • CrewAI · Agent Framework
  • Daytona · Development Sandbox

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about CrewAI & Daytona Multi-Agent Capital Allocation Engine