Voice-Enabled Tanker Allocation Agent using OpenAI Agents SDK and Retell AI
When managing immediate short-term crude exports across Basra terminals, operations teams require rapid voice-driven updates and automated dispatch actions. Build a voice-interactive tanker allocation agent that leverages Retell AI for conversational interaction and OpenAI Agents SDK for background execution and tool calling. The agent must parse real-time voice queries from terminal dispatchers, check berth schedules, verify 3-month temporary export permits, and automatically queue vessel loading sequences.
What you are building
Develop a real-time voice and agent dispatch system for oil tanker loading schedules using OpenAI Agents SDK and Retell AI.
How work is evaluated
Tests voice transcript intent mapping and accurate berth assignment generation.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
verify_berth_assignment
Ensure requested berth is allocated accurately.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
intent_accuracy
Accuracy of intent extraction from spoken input. • target: 0.98 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Learning goals
Implement real-time voice agent workflows using Retell AI integration.
Build function-calling agents with OpenAI Agents SDK for tanker schedule updates.
Synchronize voice intent parsing with transactional database modifications.
Resources
Audio-transcript pair dataset of terminal dispatcher commands and terminal port states.
Challenge setup
Tests voice transcript intent mapping and accurate berth assignment generation.
Challenge input
JSON with transcript and current_terminal_state
OpenAI Agents SDK
Lightweight autonomous agent execution engine.
Retell AI
Provides real-time voice streaming integration.
Evaluated output
JSON with target_vessel, assigned_berth, action_status
- Ensure requested berth is allocated accurately.
- Accuracy of intent extraction from spoken input. • target: 0.98 • range: 0-1
- Benchmark: Voice-Logistics-Bench
- Intent Accuracy target: 0.98
- 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
- Retell AI · Required
- OpenAI · Optional
- MLflow · Optional
Evaluation contract
- verify_berth_assignment · Weight 1
- intent_accuracy · Weight 1
Metrics
- intent_accuracy · Target 0.98
Benchmarks
- Voice-Logistics-Bench
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON with transcript and current_terminal_state
Output: JSON with target_vessel, assigned_berth, action_status
Execution harness: Python
Evaluation tools
- OpenAI Agents SDK · Agent Orchestration
- Retell AI · Voice Gateway
Recipe freeze marker
No recipe freeze time is recorded.