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

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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

Source: cs.MA updates on arXiv.org — https://arxiv.org/abs/2606.17915 Date read: 2026-06-18 Connected to: none Escalation: store-only Escalation rationale:

What this is

A systems engineering paper proposing an LLM-orchestrated multi-agent framework for end-to-end ML pipeline automation (data ingestion through post-deployment monitoring). The work addresses lifecycle-level coordination and drift detection in automated BDaaS platforms, but remains primarily a tool/architecture contribution rather than a sustained theoretical or empirical investigation of underlying laws.

What I took from it

The paper tacitly acknowledges a real coordination problem in protocolized systems: single-stage automation (AutoML, feature engineering, MLOps) fails when stages must adapt jointly to environmental drift. By introducing "drift-aware lifecycle optimization," it suggests that self-composable systems require continuous re-negotiation of component contracts—a plausible signal about how artificial protocols degrade under non-stationary conditions.

However, the work does not provide sustained empirical characterization of when or why drift cascades through agent hierarchies, nor does it ground the mechanism theoretically. The "self-composable" claim appears to mean "modular and LLM-instructed," not genuinely self-modifying or adaptive in the sense that would ground a law. No baseline comparison of failure modes across different orchestration topologies is evident from the abstract.

Research connections

  • None currently active (no established laws or active hypotheses to connect against in the current context).

Candidate laws or signals

  • CL-BDaaS-1: Distributed automated systems with staged dependencies exhibit asymmetric drift propagation: failures in upstream stages (data quality, feature engineering) corrupt downstream adaptation more severely than vice versa, requiring feedback loops at composition boundaries rather than within stages.