Build a D2C Reverse Logistics Automation Agent with OpenAI Agents SDK
At Inc42's D2C Summit, logistics leaders emphasized reducing Return to Origin (RTO) costs for Indian retail brands. In this challenge, you will implement an intelligent reverse logistics triage agent using the OpenAI Agents SDK. The system intercepts user return requests, assesses return eligibility based on item condition photos and delivery timelines, dynamically routes return packages to nearest refurbishment hubs, and calculates restocking economics.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an autonomous D2C reverse logistics triage agent using OpenAI Agents SDK tool execution.
How work is evaluated
Evaluates return authorization and reverse shipping route optimization.
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.
size_exchange_preference_test
Verifies that size discrepancy triggers an exchange offer prior to full cash refund.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
rto_reduction_efficacy
Percentage of returns successfully converted to exchanges or retention. • target: 0.85 • 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 agents with custom tool function handlers.
Process return reason telemetry and damage assessment inputs.
Calculate RTO prevention strategies (instant exchange discounts, local repair).
Output optimal reverse shipping carrier routes.
Reference links and supporting material
D2C customer order and return ticket history dataset with reason codes and carrier costs.
Challenge setup
Evaluates return authorization and reverse shipping route optimization.
Challenge input
JSON with order_id, item_category, return_reason, item_value_inr, customer_rto_score.
OpenAI Agents SDK
Required SDK for function execution loops.
Evaluated output
JSON with action_type, refund_amount_inr, target_hub_id.
- Verifies that size discrepancy triggers an exchange offer prior to full cash refund.
- Percentage of returns successfully converted to exchanges or retention. • target: 0.85 • range: 0-1
- Benchmark: Reverse Logistics Benchmark
- Rto Reduction Efficacy target: 0.85
- 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
- OpenAI · Required
- Zed · Optional
- AutoML (H2O) · Optional
Evaluation contract
- size_exchange_preference_test · Weight 1
- rto_reduction_efficacy · Weight 1
Metrics
- rto_reduction_efficacy · Target 0.85
Benchmarks
- Reverse Logistics Benchmark
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON with order_id, item_category, return_reason, item_value_inr, customer_rto_score.
Output: JSON with action_type, refund_amount_inr, target_hub_id.
Execution harness: Python
Evaluation tools
- OpenAI Agents SDK · Agent Framework
Recipe freeze marker
No recipe freeze time is recorded.