LNG Procurement & Decoupled Execution with OpenAI Agents SDK & Akash Network
Adnoc selected major contractors for a new LNG export project in the UAE. Build a decentralized, high-availability supply chain agent using OpenAI Agents SDK deployed on Akash Network to track long-lead procurement items (e.g., cryogenic exchangers) and calculate risk delays. Achieve sub-second query response and continuous uptime.
What you are building
The core problem, expected build, and operating context for this challenge.
Deploy an OpenAI Agents SDK application on Akash Network cloud compute to maintain decentralized tracking of LNG project supply chains.
How work is evaluated
Tests agent latency and lead time risk calculation on decentralized compute nodes.
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.
latency_check
Ensures query response completes within target threshold
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
risk_prediction_accuracy
Accuracy compared against actual historical delay patterns • target: 0.88 • 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 multi-agent turn workflows using OpenAI Agents SDK.
Containerize and deploy the agent workflow onto Akash Network decentralized compute.
Evaluate long-lead LNG equipment delivery milestones and supplier lead times.
Implement failover and dynamic function execution across distributed nodes.
Reference links and supporting material
Supplier lead time records and manufacturing milestones for LNG plant components.
How this agent runs
Tests agent latency and lead time risk calculation on decentralized compute nodes.
Challenge input
JSON order details including supplier location, promised date, and shipping status
OpenAI Agents SDK
Provides robust multi-agent orchestration.
Akash Network
Decentralized hosting infrastructure.
Evaluated output
JSON assessment with critical path delay impact in days
- Ensures query response completes within target threshold
- Accuracy compared against actual historical delay patterns • target: 0.88 • range: 0-1
- Benchmark: SupplyChainAgent-Bench
- Risk Prediction Accuracy target: 0.88
- 1 public reference case
- Docker execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Akash Network · Required
- OpenAI · Optional
- Zed · Optional
Evaluation contract
- latency_check · Weight 1
- risk_prediction_accuracy · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.