OpenAI Agents SDK Suburban Land Assembly Feasibility Agent
Faith-based capital and institutional funds are targeting suburban land parcels for multi-decade land assembly plays. Build an autonomous land assembly agent using OpenAI Agents SDK to analyze adjacent suburban land parcels, estimate hold yields, and evaluate zoning change risks.
What you are building
The core problem, expected build, and operating context for this challenge.
Construct an autonomous suburban land assembly agent using OpenAI Agents SDK to model long-horizon land acquisition strategies.
How work is evaluated
Evaluates accuracy of land assembly contiguity analysis and long-term hold ROI.
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.
contiguity_sum_check
Checks that aggregated acreage matches sum of individual parcel areas.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
assembly_cost_accuracy
Accuracy of total acquisition cost calculations across all parcels. • 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
Implement OpenAI Agents SDK runner with function-calling tools
Build parcel aggregation algorithms that compute continuous parcel boundaries
Model 20-30 year long-horizon land holding costs and tax abatement potentials
Generate automated zoning risk profiles for multi-parcel suburban assemblies
Reference links and supporting material
Parcel boundary and tax assessment tabular data for 30 suburban land assembly opportunities.
How this agent runs
Evaluates accuracy of land assembly contiguity analysis and long-term hold ROI.
Challenge input
JSON containing parcel list with parcel_id, acreage, tax_assessed_value, contiguity_ids
OpenAI Agents SDK
Provides official OpenAI agent tool declarations and handoff orchestration.
Evaluated output
JSON with combined_acreage, total_acquisition_cost, assembly_viable boolean
- Checks that aggregated acreage matches sum of individual parcel areas.
- Accuracy of total acquisition cost calculations across all parcels. • target: 0.98 • range: 0-1
- Benchmark: SuburbanLandAssemblyBench
- Assembly Cost Accuracy target: 0.98
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- OpenAI · Required
- LiveKit · Optional
- NNI (Neural Network Intelligence) · Optional
Evaluation contract
- contiguity_sum_check · Weight 1
- assembly_cost_accuracy · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.