Agentic AI Systems: A Review of Multi-Agent Reasoning, Trust, Orchestration, and Autonomous Data Science

Abstract

Agentic artificial intelligence (AI) — systems of autonomous, goal-directed software agents that plan, act, and coordinate with reduced human oversight — is moving from research prototypes toward applied data-science, perception, and decision-support pipelines. Yet the literature remains fragmented across largely disconnected strands: trust-aware orchestration of multimodal agents for visual classification, human adoption of autonomous AI agents, automated machine-learning (AutoML) pipelines for tabular data, and fully autonomous multi-agent systems for end-to-end data engineering. This review synthesizes four recent studies spanning these strands, together with the broader literature on multi-agent orchestration, retrieval-augmented generation (RAG), large-language-model (LLM)-based knowledge-graph construction, and explainable AI, to build a unified picture of agentic AI as applied to intelligent data science. We propose a four-part taxonomy — perception-level trust orchestration, human adoption of agentic systems, automated feature/model pipelines, and autonomous data-engineering agents — and compare the reviewed studies on automation level, reasoning mechanism, knowledge representation, retrieval use, trust treatment, and evaluation methodology. The synthesis shows that individual components of an agentic data-science stack (confidence calibration, retrieval-grounded re-evaluation, automated feature synthesis, autonomous pipeline repair) have each demonstrated measurable gains in isolation, but no reviewed system couples them with a persistent, multimodal knowledge-graph layer, and adoption research indicates that user trust is not the dominant driver of usage intention — a finding with direct design implications. We identify recurring gaps in provenance, explainability, benchmark standardization, and graph-grounded reasoning, and outline an integrated conceptual framework for the reviewed research landscape, positioning closer integration of agentic data science with knowledge-graph representation as an open research trajectory rather than a solved problem.