Multi-Agent Real Estate Underwriting Engine with AutoGen and Galileo
Commercial real estate firms face delayed underwriting decisions due to fragmented market metrics and valuation reports. Build a multi-agent system using AutoGen and Galileo to parse zoning, debt terms, and asset cash flows into automated investment memos. Achieve target underwriting memo generation with under 5% variance from expert benchmarks.
What you are building
The core problem, expected build, and operating context for this challenge.
Construct an AutoGen multi-agent pipeline integrated with Galileo observability to evaluate commercial acquisition targets and generate quantitative deal memos.
How work is evaluated
Evaluates the precision of debt metrics calculation and memo completeness against benchmark deal sheets.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
dscr_calculation_check
Validates DSCR is calculated within +/- 2% accuracy
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
irr_variance
Percentage variance between predicted IRR and target IRR • target: 0.01 • range: 0-0.05
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 multi-agent conversations between market research, debt compliance, and cash flow modeling agents using AutoGen
Integrate Galileo evaluation metrics to continuously monitor prompt quality, hallucination rate, and execution correctness
Parse unstructured commercial real estate dossiers and extract key cap rates, NOIs, and IRR targets
Synthesize automated, audit-ready investment memos with structured financial outputs
Reference links and supporting material
Synthetic commercial real estate asset packages including balance sheets, lease roll reports, and debt term sheets.
How this agent runs
Evaluates the precision of debt metrics calculation and memo completeness against benchmark deal sheets.
Challenge input
JSON containing property financial statements, NOI history, and debt terms
AutoGen
Multi-agent conversation orchestration for real estate tasks.
Galileo
Tracking evaluation metrics and detecting hallucination in agent output.
Evaluated output
JSON containing project_irr, target_dscr, net_equity_multiple, and risk_flags
- Validates DSCR is calculated within +/- 2% accuracy
- Percentage variance between predicted IRR and target IRR • target: 0.01 • range: 0-0.05
- Benchmark: CRE Financial Underwriting Benchmark
- Irr Variance target: 0.01
- 1 public reference case
- Python execution harness
- Python sandbox
View technical recipe
Configured tools
- AutoGen · Optional
- Fixie · Optional
- Galileo · Required
- Galileo · Required
Evaluation contract
- dscr_calculation_check · Weight 1
- irr_variance · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.