Build PLI Scheme Incentive Compliance Auditor with OpenAI Agents SDK
EV manufacturing incentive disbursement requires strict verification of domestic value addition and localized sourcing claims. Build an autonomous audit agent using OpenAI Agents SDK to parse production invoices and yield compliance audit reports with 100% mathematical precision.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an automated manufacturing compliance auditor using the OpenAI Agents SDK and function tools.
How work is evaluated
Evaluates domestic value addition percentage calculations and subsidy eligibility decisions.
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.
dva_calculation_exactness
Confirms mathematical exactness of local value share calculation
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
audit_accuracy
Accuracy score of PLI subsidy eligibility decisions • target: 1 • 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 autonomous compliance verification agents using the OpenAI Agents SDK
Implement multi-turn function calls for invoice reconciliation and calculation
Generate automated government PLI scheme audit compliance summaries
Reference links and supporting material
Simulated component bill-of-materials and supply chain invoices for electric vehicle assembly.
How this agent runs
Evaluates domestic value addition percentage calculations and subsidy eligibility decisions.
Challenge input
JSON containing local_cost_inr, imported_cost_inr, total_assembly_cost_inr
OpenAI Agents SDK
Provides function-calling agent runtime and tool execution
Evaluated output
JSON containing dva_percentage, eligible_for_subsidy, and payout_inr
- Confirms mathematical exactness of local value share calculation
- Accuracy score of PLI subsidy eligibility decisions • target: 1 • range: 0-1
- Benchmark: PLI-Audit-Bench
- Audit Accuracy target: 1
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- OpenAI · Required
- Aim · Optional
- Zed · Optional
Evaluation contract
- dva_calculation_exactness · Weight 1
- audit_accuracy · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.