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Build an Intelligent Crude Export Clearance Agent with LlamaIndex and Arcee AI

Iraq's approval of a three-month mechanism for crude oil exports via local and international trading firms introduces temporary regulatory parameters and dynamic counterparty verification rules. Developers must build a domain-specific retrieval-augmented generation (RAG) system capable of parsing complex bilingual petroleum sales agreements, official gazettes, and SOMO (State Organization for Marketing of Oil) allocation notices. Using LlamaIndex and Arcee AI, you will index structured and unstructured export rules, implement contextual routing, and orchestrate an agent that verifies whether proposed local and foreign trading firms meet interim capitalization, compliance, and lift volume quotas.

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What you are building

Implement a bilingual crude oil export clearance agent using LlamaIndex contextual retrieval and Arcee AI workflow automation.

How work is evaluated

Evaluation

Evaluates accuracy of trade firm qualification and quota clearance under the 3-month mechanism.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

check_regulatory_compliance

Ensure firm meets minimum capital and quota constraints.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

clearance_accuracy

Accuracy across 50 simulated firm evaluation scenarios. • target: 0.95 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • Master contextual RAG indexing for complex bilingual petroleum trade regulatory documents.

  • Implement multi-document routing and metadata filtering using LlamaIndex.

  • Integrate Arcee AI workflows to synthesize compliance verdicts and verification records.

Resources

Dataset notes

Synthetic dataset of 50 GCC crude trading company profiles and interim 3-month export rules.

Challenge setup

Evaluates accuracy of trade firm qualification and quota clearance under the 3-month mechanism.

Challenge input

JSON containing firm_profile and requested_lift_volume_bpd

LlamaIndex

Provides robust data framework for complex RAG operations.

Arcee AI

High efficiency specialized domain models for trade operations.

Evaluated output

JSON with clearance_status, violated_clauses, and recommended_allocation

Evaluation checks
  • Ensure firm meets minimum capital and quota constraints.
  • Accuracy across 50 simulated firm evaluation scenarios. • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Benchmark: GCC-Energy-Trade-Bench
  • Clearance Accuracy target: 0.95
  • 1 public reference case
Configured runtime
  • 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

Action Space
  • Arcee AI · Required
  • Llama Index · Optional
  • Exa · Optional

Evaluation contract

  • check_regulatory_compliance · Weight 1
  • clearance_accuracy · Weight 1

Metrics

  • clearance_accuracy · Target 0.95

Benchmarks

  • GCC-Energy-Trade-Bench
  • energy

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing firm_profile and requested_lift_volume_bpd

    Output: JSON with clearance_status, violated_clauses, and recommended_allocation

    Execution harness: Python

Evaluation tools

  • LlamaIndex · Orchestration & RAG Indexing
  • Arcee AI · SLM Inference

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Build an Intelligent Crude Export Clearance Agent with LlamaIndex and Arcee AI