New LLM-Orchestrated Multi-Agent Framework Enhances BDaaS Lifecycle Automation

By PulseAugur Editorial · [2 sources] · 2026-06-16 13:34

Researchers have developed a new framework for Big-Data-as-a-Service (BDaaS) that utilizes a multi-agent system orchestrated by a central LLM. This system aims to automate and improve the reliability of the entire data engineering and MLOps lifecycle, from ingestion to post-deployment monitoring and drift detection. The proposed architecture decomposes tasks into specialized agents, enhancing lifecycle-level orchestration, artifact governance, and human oversight compared to existing single-agent or AutoML-only approaches. AI

IMPACT This framework could streamline and improve the reliability of complex data pipelines, potentially accelerating the deployment and maintenance of AI models in production environments.

RANK_REASON The cluster contains a research paper detailing a novel framework for data engineering and MLOps.

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

arXiv cs.AI TIER_1 English(EN) · Aueaphum Aueawatthanaphisut, Badri Raj Lamichhane · 2026-06-17 04:00

Trustworthy Self-Composable Big-Data-as-a-Service: An LLM-Orchestrated Multi-Agent Framework for Automated Data Engineering, AutoML, MLOps Deployment, and Drift-Aware Lifecycle Optimization

arXiv:2606.17915v1 Announce Type: cross Abstract: Big-Data-as-a-Service (BDaaS) platforms require re liable automation across data ingestion, cleaning, feature engi neering, model development, deployment, and post-deployment monitoring. However, existing LLM-based data science ag…
arXiv cs.MA (Multiagent) TIER_1 English(EN) · Badri Raj Lamichhane · 2026-06-16 13:34

Trustworthy Self-Composable Big-Data-as-a-Service: An LLM-Orchestrated Multi-Agent Framework for Automated Data Engineering, AutoML, MLOps Deployment, and Drift-Aware Lifecycle Optimization

Big-Data-as-a-Service (BDaaS) platforms require re liable automation across data ingestion, cleaning, feature engi neering, model development, deployment, and post-deployment monitoring. However, existing LLM-based data science agents and AutoML systems mainly focus on isolated w…

COVERAGE [2]

Trustworthy Self-Composable Big-Data-as-a-Service: An LLM-Orchestrated Multi-Agent Framework for Automated Data Engineering, AutoML, MLOps Deployment, and Drift-Aware Lifecycle Optimization

Trustworthy Self-Composable Big-Data-as-a-Service: An LLM-Orchestrated Multi-Agent Framework for Automated Data Engineering, AutoML, MLOps Deployment, and Drift-Aware Lifecycle Optimization

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