Analyze Faith-Based Suburban Land Plays with OpenAI Agents SDK & Google Jules
Evaluating long-horizon Suburban land plays requires analyzing complex municipal zoning laws, faith-based capital sources, and entitlement risks. Build an OpenAI Agents SDK system with Google Jules workflow automation to parse land records and generate entitlement risk scores with 90% precision.
What you are building
Utilize OpenAI Agents SDK alongside Google Jules code environment assistance to parse parcel records and calculate suburban land entitlement risk levels.
How work is evaluated
Evaluates parcel risk scoring precision and zoning constraint detection accuracy.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
Risk Classification Test
Ensures entitlement risk level matches calculated threshold rules.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Risk Precision Score
Precision of identified key zoning risks. • target: 0.9 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Learning goals
Implement multi-turn autonomous agents using OpenAI Agents SDK for land parcel assessment.
Leverage Google Jules automated software execution tasks to process GIS shapefiles and zoning text.
Formulate long-horizon yield and entitlement probability risk matrices.
Resources
Suburban land parcel dataset including acreage, current zoning codes, target density specifications, and jurisdiction rules.
Challenge setup
Evaluates parcel risk scoring precision and zoning constraint detection accuracy.
Challenge input
JSON with parcel_id, acreage, current_zoning, target_zoning, municipal_jurisdiction
OpenAI Agents SDK
Assigned framework for autonomous multi-turn agents.
Google Jules
Provides code execution and background software tooling.
Evaluated output
JSON with entitlement_risk_level (Low/Medium/High), estimated_entitlement_months, key_risks
- Ensures entitlement risk level matches calculated threshold rules.
- Precision of identified key zoning risks. • target: 0.9 • range: 0-1
- Benchmark: Land_Entitlement_Eval
- Risk Precision Score target: 0.9
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Shows the first task, up to three tools, and up to three evidence items.
View technical recipe
Configured tools
- Google Jules · Required
- OpenAI · Optional
- BoTorch · Optional
- Google Jules · Required
Evaluation contract
- Risk Classification Test · Weight 1
- Risk Precision Score · Weight 1
Metrics
- Risk Precision Score · Target 0.9
Benchmarks
- Land_Entitlement_Eval
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON with parcel_id, acreage, current_zoning, target_zoning, municipal_jurisdiction
Output: JSON with entitlement_risk_level (Low/Medium/High), estimated_entitlement_months, key_risks
Execution harness: Python
Evaluation tools
- OpenAI Agents SDK · Agent Framework
- Google Jules · AI Engineering Tooling
Recipe freeze marker
No recipe freeze time is recorded.