Long-Horizon Land Parcel Assembly Agent using OpenAI Agents SDK
Long-term institutional investors evaluate complex suburban land assembly opportunities with fragmented ownership. Build an autonomous agent using the OpenAI Agents SDK to parse property tax records, evaluate zoning aggregation rules, and model long-horizon IRR without execution errors.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop an autonomous multi-turn land aggregation agent using the OpenAI Agents SDK and function-calling workflows.
How work is evaluated
Evaluates land parcel aggregation suitability and long-horizon IRR calculations.
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.
total_cost_math
Total acquisition cost equals total_acreage * average_cost_per_acre
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
irr_calculation_accuracy
Precision of 10-year calculated internal rate of return against financial formula • target: 0.95 • 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 autonomous agent with the OpenAI Agents SDK using function calling and auto-tool execution.
Analyze tax map spatial layouts to identify adjacent sub-parcels suitable for assembly.
Calculate assembled land acquisition cost and project 10-year holding IRR.
Ensure robust agent tool response handling across multi-turn user sessions.
Reference links and supporting material
Suburban parcel boundary maps, tax assessment records, and zoning aggregation regulations.
How this agent runs
Evaluates land parcel aggregation suitability and long-horizon IRR calculations.
Challenge input
JSON containing parcel_ids array, total_acreage, average_cost_per_acre, projected_exit_psf
OpenAI Agents SDK
Framework assignment for building OpenAI autonomous function agents.
Evaluated output
JSON with assembly_viable boolean, total_acquisition_cost, projected_10yr_irr
- Total acquisition cost equals total_acreage * average_cost_per_acre
- Precision of 10-year calculated internal rate of return against financial formula • target: 0.95 • range: 0-1
- Benchmark: OpenAI-Agent-Bench
- Irr Calculation Accuracy target: 0.95
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- OpenAI · Required
- Ray Tune · Optional
- LiveKit · Optional
Evaluation contract
- total_cost_math · Weight 1
- irr_calculation_accuracy · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.