Build a Stadium Procurement Risk Inspector with Claude Agents SDK
Managing tight deadlines for $291m World Cup stadium infrastructure requires automated design and tender package auditing. Build an autonomous agent using Claude Agents SDK with extended thinking to identify scope gaps and verify compliance with FIFA infrastructure standards.
What you are building
The core problem, expected build, and operating context for this challenge.
Utilize Claude Agents SDK extended thinking capabilities to inspect stadium project tender packages and generate risk mitigation matrix reports.
How work is evaluated
Evaluates the agent's ability to detect non-compliant items in stadium construction tenders.
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.
violation_detection_check
Ensure non-compliance violation is flagged
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
audit_precision
Precision of compliance rule detection • target: 0.98 • range: 0.9-1
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 Claude Agents SDK runner loops with explicit extended thinking limits
Parse multi-disciplinary stadium design packages (structural, MEP, crowd circulation)
Audit contractor proposals against FIFA 2030 technical requirements
Generate prioritized risk scorecards for project management offices
Reference links and supporting material
Moroccan stadium tender notices, civil design metrics, and FIFA 2030 technical infrastructure manuals.
Challenge setup
Evaluates the agent's ability to detect non-compliant items in stadium construction tenders.
Challenge input
JSON containing stadium specs
Claude Agents SDK
Provides extended thinking models ideal for deep design verification
Evaluated output
JSON compliance report
- Ensure non-compliance violation is flagged
- Precision of compliance rule detection • target: 0.98 • range: 0.9-1
- Benchmark: InfrastructureAuditBench
- Audit Precision 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
- StackAI · Required
- AutoML (H2O) · Optional
- Deepgram · Optional
Evaluation contract
- violation_detection_check · Weight 1
- audit_precision · Weight 1
Metrics
- audit_precision · Target 0.98
Benchmarks
- InfrastructureAuditBench
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing stadium specs
Output: JSON compliance report
Execution harness: Python
Evaluation tools
- Claude Agents SDK · Agent Framework
Recipe freeze marker
No recipe freeze time is recorded.