This work presents DeepEye, a workflow-centric agentic data system that turns user intents into transparent and steerable analytical workflows and develops DataMagic as the system’s Video Generator, a declarative multi-agent method that improves data-video quality.
In today's fast-paced environment, the ability to swiftly access, understand, and act on data is no longer optional; it is essential. Yet most organizations remain data-rich but insight-poor, constrained by the complexity of querying, interpreting, and explaining enterprise-scale information. We present Polaris, a supervisor-led multi-agent framework for conversational enterprise analytics that bridges this gap. Polaris introduces Dynamic Task Coordination (DTC), a decision-theoretic orchestration layer that models agent-task assignment as adaptive bipartite matching, enabling real-time coordination, recovery, and optimization across specialized agents for querying, visualization, and reasoning. By coupling DTC with reason-first, ReAct-style agents, Polaris transforms natural-language queries into coherent analytical workflows that not only retrieve and visualize data but also explain the underlying"why."Evaluation on structured enterprise datasets demonstrates high semantic fidelity and answer relevancy, underscoring the potential of multi-agent orchestration to deliver trustworthy, end-to-end business intelligence at scale.
K. VaruniH., Soham Sarkar, J. Kumar et al.· 0 citations
Comparisons against stronger model and coding-agent competitors further indicate that both domain-specific agent runtime structure and foundation-model strength matter for autonomous data analysis.
DataClawEval is introduced, the first comprehensive benchmark designed specifically to evaluate the end-to-end task completion capabilities of autonomous agents in real-world data engineering scenarios, and it comprises 100 rigorous, end-to-end tasks spanning five execution engines.
Debin Meng, Jiaming Yang, Zefang Zong et al.· 0 citations
The AgenticData system, an agentic data system that enables natural-language query analytics over heterogeneous data sources, is introduced and its ability to handle diverse data sources accurately and efficiently is illustrated.
Peiyao Zhou, Ji Sun, Yaoqiang Xu et al.· 0 citations
Modern data platforms rely on pipeline-oriented architectures that are rigid, hard to adapt, and lack native auditability. We present Agentic Data Services, a control-planedriven architecture for Big Data as a Service (BDaaS) that models workflows as adaptive, policy-aware service entities rather than static directed acyclic graphs (DAGs). The architecture combines (i) a dual-record execution model adapted from pharmaceutical batch manufacturing-versioned Master Batch Records (MBRs) for workflow definition and immutable Electronic Batch Records (EBRs) for execution traces-and (ii) workflow-level semantic caching that reuses results across semantically similar requests. We implement the system as Agentic DataHub, a set of Rustbased microservices deployed on Kubernetes, and evaluate the semantic-caching component on a reproducible benchmark using sentence-transformer embeddings (all-MiniLM-L6-v2) and a FAISS flat inner-product index. For clustered workloads-semantically related requests grouped into 10 clusters with 70% intra-cluster similarity-the cache reduces backend requests by 81% and median latency by 93%, with 40% P95 latency reduction. We discuss generalization across domains and the architectural constraints that bound these results.
Alexander Chernov· International Conference on...· 0 citations
Modern data architecture is fundamentally constrained by a legacy assumption: that the ultimate con- sumer of information is a human being. Consequently, cur- rent paradigms disproportionately allocate computational resources to human-legible interfaces, rigid API schemas, and highly abstracted intermediate data states designed primarily for manual oversight and debugging. With the rapid ascendancy of autonomous agentic workflows, this human-centric design introduces severe inefficiencies in latency, throughput, and structural complexity. (1) This paper introduces an Agent-First Data Architecture (AFDA), a paradigm shift that re-engineers data stor- age, transport, and synthesis exclusively for machine-to- machine optimization. We explore the systematic disman- tling of the traditional visual application layer, demon- strating how autonomous agents render fixed user in- terfaces and rigid middleware pipelines obsolete through on-the-fly, task-specific data computation. Furthermore, we analyze the efficiencies gained by transitioning from human-readable protocols (such as JSON or XML) to non- human-readable intermediate states. Finally, we address the architectural flattening of the modern software stack and confront the emerging challenges of this shift, specif- ically the ”black box” debugging crisis and the necessity of specialized Observer Agents for forensic translation. Ultimately, we argue that shifting from a human-centric to an agent-first paradigm is a prerequisite for unlocking the true scaling laws of decentralized machine intelligence. (2)
Anand Ganesh· International Journal of Art...· 0 citations