MultiFluxAI Enhancing Platform Engineering with Advanced Agent-Orchestrated Retrieval Systems

• URL: http://arxiv.org/abs/2508.21307v1
• Date: Fri, 29 Aug 2025 02:08:36 GMT
• Title: MultiFluxAI Enhancing Platform Engineering with Advanced Agent-Orchestrated Retrieval Systems
• Authors: Sri Ram Macharla, Sridhar Murthy J, Anjaneyulu Pasala,
• Abstract summary: MultiFluxAI is an innovative AI platform developed to address the challenges of managing and integrating vast, disparate data sources in product engineering across application domains.<n>It addresses both current and new service related queries that enhance user engagement in the digital ecosystem.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: MultiFluxAI is an innovative AI platform developed to address the challenges of managing and integrating vast, disparate data sources in product engineering across application domains. It addresses both current and new service related queries that enhance user engagement in the digital ecosystem. This platform leverages advanced AI techniques, such as Generative AI, vectorization, and agentic orchestration to provide dynamic and context-aware responses to complex user queries.

Related papers

• Digital Twins & ZeroConf AI: Structuring Automated Intelligent Pipelines for Industrial Applications [3.534869097377701]
  This work proposes a modular and interoperable solution that enables seamless AI pipeline integration into Cyber-Physical Systems.<n>We introduce the concept of Zero configuration (ZeroConf) AI pipelines, where DTs orchestrate data management and intelligent augmentation.<n>The approach is demonstrated in a MicroFactory scenario, showing support for concurrent ML models and dynamic data processing.
  arXiv  Detail & Related papers  (2026-02-04T10:11:06Z)
• Advances and Frontiers of LLM-based Issue Resolution in Software Engineering: A Comprehensive Survey [59.3507264893654]
  Issue resolution is a complex Software Engineering task integral to real-world development.<n> benchmarks like SWE-bench revealed this task as profoundly difficult for large language models.<n>This paper presents a systematic survey of this emerging domain.
  arXiv  Detail & Related papers  (2026-01-15T18:55:03Z)
• A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks [49.90474228895655]
  Cloud-edge-terminal collaborative intelligence (CETCI) is a fundamental paradigm within the artificial intelligence of things (AIoT) community.<n>CETCI has made significant progress with emerging AIoT applications, moving beyond isolated layer optimization to deployable collaborative intelligence systems.<n>This survey describes foundational architectures, enabling technologies, and scenarios of CETCI paradigms, offering a tutorial-style review for CISAIOT beginners.
  arXiv  Detail & Related papers  (2025-08-26T08:38:01Z)
• Deep Research Agents: A Systematic Examination And Roadmap [109.53237992384872]
  Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
  arXiv  Detail & Related papers  (2025-06-22T16:52:48Z)
• AI Flow: Perspectives, Scenarios, and Approaches [51.38621621775711]
  We introduce AI Flow, a framework that integrates cutting-edge IT and CT advancements.<n>First, device-edge-cloud framework serves as the foundation, which integrates end devices, edge servers, and cloud clusters.<n>Second, we introduce the concept of familial models, which refers to a series of different-sized models with aligned hidden features.<n>Third, connectivity- and interaction-based intelligence emergence is a novel paradigm of AI Flow.
  arXiv  Detail & Related papers  (2025-06-14T12:43:07Z)
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [58.50944604905037]
  Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
  arXiv  Detail & Related papers  (2025-05-03T13:55:38Z)
• Beyond Text: Implementing Multimodal Large Language Model-Powered Multi-Agent Systems Using a No-Code Platform [0.0]
  This study proposes the design and implementation of a multimodal LLM-based Multi-Agent System (MAS)<n>This research develops a No-Code-based Multi-Agent System designed to enable users without programming knowledge to easily build and manage AI systems.<n>The study examines various use cases to validate the applicability of AI in business processes, including code generation from image-based notes, Advanced RAG-based question-answering systems, text-based image generation, and video generation.
  arXiv  Detail & Related papers  (2025-01-01T06:36:56Z)
• Agentic AI-Driven Technical Troubleshooting for Enterprise Systems: A Novel Weighted Retrieval-Augmented Generation Paradigm [0.0]
  This paper presents a novel agentic AI solution built on a Weighted Retrieval-Augmented Generation (RAG) Framework tailored for enterprise technical troubleshooting.<n>By dynamically weighting retrieval sources such as product manuals, internal knowledge bases, FAQ, and troubleshooting guides, the framework prioritizes the most relevant data.<n>Preliminary evaluations on large enterprise datasets demonstrate the framework's efficacy in improving troubleshooting accuracy, reducing resolution times, and adapting to varied technical challenges.
  arXiv  Detail & Related papers  (2024-12-16T17:32:38Z)
• Collaborative AI in Sentiment Analysis: System Architecture, Data Prediction and Deployment Strategies [3.3374611485861116]
  Large language model (LLM) based artificial intelligence technologies have been a game-changer, particularly in sentiment analysis. However, integrating diverse AI models for processing complex multimodal data and the associated high costs of feature extraction presents significant challenges. This study introduces a collaborative AI framework designed to efficiently distribute and resolve tasks across various AI systems.
  arXiv  Detail & Related papers  (2024-10-17T06:14:34Z)
• Integrating Artificial Intelligence into Operating Systems: A Comprehensive Survey on Techniques, Applications, and Future Directions [16.28550500194823]
  fusion of Artificial Intelligence with Operating Systems emerges as a critical frontier for innovation.<n>Current status of AI-OS integration, accentuating its pivotal role in steering the evolution of advanced computing paradigms.<n>Future prospects of Intelligent Operating Systems, debating how groundbreaking OS designs will usher in novel possibilities.
  arXiv  Detail & Related papers  (2024-07-19T05:29:34Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.