LN: Zhang et al. (2024) — Intelligent Spark Agents: A Modular LangGraph Framework
Bibliographic Reference
Citation: Zhang, W., Liu, H., Chen, M., & Wang, J. (2024). Intelligent Spark agents: A modular LangGraph framework for scalable, visualized, and enhanced big data machine learning workflows. arXiv:2412.01490. https://arxiv.org/abs/2412.01490 (This paper is in the PUMA project knowledge PDFs as “Intelligent_Spark_Agents_A_Modular_LangGraph_Framework_2412.01490v4.pdf”)
Pass 1 — Bird’s Eye View (5 Cs)
| C | Assessment |
|---|---|
| Category | Framework + system |
| Context | Applies LangGraph to orchestrate Apache Spark ML workflows |
| Correctness | Evaluated on ML pipeline tasks. |
| Contributions | (1) LangGraph-based modular agent orchestration for Spark; (2) Visualised workflow graphs; (3) Integration with SQL and ML tools |
| Clarity | Good. |
Relevance: ⭐⭐⭐
Demonstrates LangGraph’s viability for modular agentic pipelines in data processing contexts. PUMA Stage 4 uses LangGraph for the RAG-enhanced triage agent. This paper validates LangGraph as a suitable orchestration framework.
PUMA Connection
Confirms LangGraph is suitable for PUMA’s cyclic agentic workflows (triage → retrieval → classify → reflect). Technical reference for the LangGraph implementation.