Post-Disaster Rebuild Cost and Muni Bond Underwriting Agent with OpenAI Agents SDK
Nonprofit housing developers financing disaster rebuilds in wildfire zones face delayed funding commitments due to unaligned debt coverage models. Build an autonomous agent system using the OpenAI Agents SDK to parse contractor estimates and compute municipal bond debt coverage ratios (DSCR). Achieve accurate DSCR calculations across 50 simulated rebuild scenarios.
What you are building
The core problem, expected build, and operating context for this challenge.
Construct an OpenAI Agents SDK tool pipeline that processes disaster rebuild contractor bids and computes municipal bond debt service feasibility.
How work is evaluated
Evaluates calculation precision for debt service coverage ratio (DSCR) and bond tranche sizing.
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.
dscr_value_check
Checks DSCR equals projected_net_revenues / annual_debt_service within 0.01 tolerance
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
dscr_accuracy
Accuracy percentage of municipal bond underwriting metrics • target: 0.98 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Build an agent system using OpenAI Agents SDK (`openai-agents` / Function Calling tools)
Parse home rebuilding cost schedules and infrastructure debt service amortization tables
Calculate municipal bond Debt Service Coverage Ratios (DSCR) and loan-to-cost (LTC) metrics
Generate automated compliance certificates for tax-exempt municipal bond issuance
Reference links and supporting material
Post-disaster home rebuild contractor bid estimates combined with municipal bond debt schedule data.
Challenge setup
Evaluates calculation precision for debt service coverage ratio (DSCR) and bond tranche sizing.
Challenge input
JSON containing total_rebuild_cost, insurance_payout, bond_issuance_amount, annual_debt_service, projected_net_revenues
OpenAI Agents SDK
OpenAI's official framework for building tool-enabled agents.
Evaluated output
JSON containing net_bond_requirement, dscr, bond_issuance_approved
- Checks DSCR equals projected_net_revenues / annual_debt_service within 0.01 tolerance
- Accuracy percentage of municipal bond underwriting metrics • target: 0.98 • range: 0-1
- Benchmark: Municipal Bond Underwriting Benchmark
- Dscr Accuracy target: 0.98
- 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.
View technical recipe
Configured tools
- OpenAI · Required
- Hume AI · Optional
- TPOT · Optional
Evaluation contract
- dscr_value_check · Weight 1
- dscr_accuracy · Weight 1
Metrics
- dscr_accuracy · Target 0.98
Benchmarks
- Municipal Bond Underwriting Benchmark
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing total_rebuild_cost, insurance_payout, bond_issuance_amount, annual_debt_service, projected_net_revenues
Output: JSON containing net_bond_requirement, dscr, bond_issuance_approved
Execution harness: Python
Evaluation tools
- OpenAI Agents SDK · agent_framework
Recipe freeze marker
No recipe freeze time is recorded.