Multi-Agent Orchestration for Autonomous Data Pipelines: A Systems Architecture for Self-Healing, Context-Aware, and Resilient Data Processing
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V7I1P107Keywords:
Multi-Agent Orchestration, Autonomous Data Pipelines, Self-Healing Systems, Context-Aware Processing, Resilient Data ArchitecturesAbstract
Modern enterprise data platforms increasingly operate under conditions of extreme scale, heterogeneity, and uncertainty. Traditional data pipeline orchestration frameworks rely on static Directed Acyclic Graphs (DAGs) and deterministic retry semantics, which are fundamentally misaligned with environments characterized by schema volatility, infrastructure churn, and non-stationary workloads. This paper presents a comprehensive architectural model for Multi-Agent Orchestrated Data Pipelines (MODP), where autonomous agents replace task-centric orchestration with goal-driven reasoning.The architecture integrates four primary subsystems: an Agent Orchestrator, a Knowledge Plane grounded in Retrieval-Augmented Generation (RAG), a Unified Feature Store, and a Causal Tracing Engine. Together, these components enable self-healing execution, dynamic schema adaptation, and causal observability across the data lifecycle. Empirical evidence from large-scale distributed systems research demonstrates that agent-based orchestration improves fault tolerance, reduces mean time to recovery (MTTR), and significantly enhances developer productivity. This work formalizes agentic data engineering as a shift from procedural execution to intent-based systems, positioning autonomous multi-agent orchestration as a foundational design principle for next-generation data platforms.
References
[1] Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
[2] Zaharia, M., Armbrust, M., Ghodsi, A., Shenker, S., & Stoica, I. (2018). Delta Lake: High-Performance ACID Table Storage over Cloud Object Stores. Proceedings of the VLDB Endowment, 12(12), 1780–1793.
[3] Abadi, M., Barham, P., Chen, J., et al. (2016). TensorFlow: A System for Large-Scale Machine Learning. Proceedings of OSDI, 265–283.
[4] Schick, T., Dwivedi-Yu, J., Dessì, R., et al. (2023). Toolformer: Language Models Can Teach Themselves to Use Tools. arXiv preprint arXiv:2302.04761.
[5] Yao, S., Zhao, J., Yu, D., et al. (2023). ReAct: Synergizing Reasoning and Acting in Language Models. arXiv preprint arXiv:2210.03629.
[6] Wu, T., Zhang, Y., Xu, Z., et al. (2023). AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation. arXiv preprint arXiv:2308.08155.
Alshawi, H., Bangalore, S., & Douglas, S. (2019). Learning to Plan for Autonomous Systems. Artificial Intelligence Journal, 276, 1–22.
[7] Dean, J., & Barroso, L. A. (2013). The Tail at Scale. Communications of the ACM, 56(2), 74–80.
[8] Barroso, L. A., Clidaras, J., & Hölzle, U. (2018). The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines (3rd ed.). Morgan & Claypool.
[9] Kleppmann, M. (2017). Designing Data-Intensive Applications. O’Reilly Media.
[10] Agarwal, S., Krishnamurthy, R., et al. (2014). Reliable and Efficient Distributed Machine Learning using Parameter Servers. Proceedings of OSDI, 583–598.
[11] Karpathy, A. (2023). Software 2.0. Distill. https://distill.pub/2017/software-2/
[12] Pearce, H., Ahmad, T., Tan, B., Dolan-Gavitt, B., & Karri, R. (2022). Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions. IEEE Symposium on Security and Privacy, 754–768.
[13] Sambasivan, N., Zahir, T., et al. (2020). Everyone Wants to Do the Model Work, Not the Data Work. Proceedings of CHI, 1–13.
[14] Sculley, D., Holt, G., Golovin, D., et al. (2015). Hidden Technical Debt in Machine Learning Systems. Proceedings of NIPS, 2503–2511.
