arXiv:2607. 07721v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) and agentic frameworks have advanced enterprise AI considerably, yet agents remain fundamentally reactive: they wait for a human query before acting.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2608. 14246v1 Announce Type: new Abstract: In today's fast-paced environment, the ability to swiftly access, understand, and act on data is no longer optional; it is essential.
arXiv:2606. 13731v1 Announce Type: new Abstract: Business intelligence (BI) increasingly combines dashboard interaction with LLM-based assistance, but these two modes often fall out of sync during multi-step analysis.
arXiv:2605.14259v3 Announce Type: replace Abstract: Applying Large Language Models (LLMs) to heterogeneous enterprise systems is hindered by hallucinations and failures in multi-hop, n-ary reasoning....
arXiv:2605.30152v2 Announce Type: replace-cross Abstract: Proactive assistants continuously decide when to intervene and what context should support the intervention. Large language model (LLM) pipel...
The paper introduces a multi‑agent platform built on CrewAI for conversational business intelligence. Five specialized agents process natural language queries, retrieve and analyze data, generate visualizations via the Model Context Protocol, and deliver actionable insights. The system includes a defense‑in‑depth security architecture, a query parameterization mechanism, and achieves 95.3% functional accuracy with a 24‑second mean latency, outperforming a single‑agent baseline by 22.6 percentage points in accuracy and 20.2% in quality.
arXiv:2607. 19297v1 Announce Type: new Abstract: This paper is a practitioner guide to graph-based workflow pathways for long-running, stateful, multi-step generative AI systems in business processes.