The Autonomous Data Enterprise: Engineering Real-Time Intelligence with Generative and Agentic AI

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Authors

P S L Narasimharao Davuluri

Keywords:

Autonomous, Artificial Intelligence, Generative Artificial Intelligence, Data Structures, Data Platforms, Data Governance

Synopsis

We stand at the threshold of a transformative era in data engineering, one where enterprises are no longer passive consumers of data but autonomous orchestrators of intelligence. The convergence of generative AI, agentic systems, and real-time analytics is fundamentally reshaping how organizations extract value from their data ecosystems.

This book emerges from years of hands-on experience architecting data platforms across healthcare, finance, and retail sectors, where the demand for instantaneous, intelligent decision-making has never been more critical. Traditional data pipelines, built on batch processing and human-dependent workflows, are giving way to self-optimizing systems that learn, adapt, and act with minimal intervention.

The "Autonomous Data Enterprise" represents more than technological advancement; it embodies a philosophical shift. It challenges the notion that data infrastructure must be laboriously managed and questions why intelligent systems cannot govern themselves. Through generative AI, we can now synthesize insights from unstructured chaos. Through agentic frameworks, we empower systems to make contextual decisions, trigger actions, and continuously improve without constant human oversight.

This book is structured to bridge theory and practice. We explore the foundational architecture of autonomous data systems, dissect real-world implementations on cloud platforms like GCP and AWS, and examine MLOps practices that enable continuous model deployment at scale. Each chapter builds upon the last, guiding you from conceptual frameworks to production-grade solutions that deliver measurable business impact.

Whether you're a data engineer seeking to modernize legacy systems, a machine learning practitioner aiming to operationalize models, or a technology leader driving digital transformation, this book provides the blueprint for building enterprises that think, learn, and evolve autonomously.

The future belongs to organizations that can transform data into action at the speed of thought. Let us begin that journey together.

 

Chapters

References

Gassmann, O., & Wincent, J. (2025). The non-human enterprise: How AI agents reshape organizations. California Management Review Insights. https://cmr.berkeley.edu/2025/10/the-non-human-enterprise-how-ai-agents-reshape-organizations

He, J., Treude, C., & Lo, D. (2025). LLM-based multi-agent systems for software engineering: Literature review, vision and the road ahead. ACM Transactions on Software Engineering and Methodology.

Bargavi, N., Athawale, S. G., Amistapuram, K., & Aitha, A. R. (2026). Safeguarding Consumer Data in Digital Insurance: Legal Frameworks and Ethical Imperatives. International Insurance Law Review, 34(S1), 272-284.

Huang, Y. (2024). Levels of AI agents: From rules to large language models. arXiv. https://arxiv.org/abs/2405.06643

Ionescu, Ș., Delcea, C., Chiriță, N., & Nica, I. (2024). Exploring the use of artificial intelligence in agent-based modeling applications: A bibliometric study. Algorithms, 17(1), 21.

Vadisetty, R., Polamarasetti, A., Rongali, S. K., kumar Prajapati, S., & Butani, J. B. (2025, May). Blockchain and Generative AI for Cloud Security: Ensuring Integrity and Transparency in Cloud Transactions. In 2025 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) (pp. 1-6). IEEE.

Joshi, S. (2026). Agentic generative artificial intelligence in enterprise organizational behavior: An integrated scholarly-practitioner mathematical and theoretical framework. Preprints.org. https://www.preprints.org/manuscript/202602.0148

Kolla, S. H. (2026). Autonomous enterprise agents: Orchestrating large and small language models for scalable decision automation. International Journal of Advances in Signal and Image Sciences.

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Front Pages

Published

10 February 2026
Copyright (c) 2026 P S L Narasimharao Davuluri

Details about the available publication format: E-Book

E-Book

ISBN-13 (15)

978-93-7185-592-1

Details about the available publication format: Book (Paperback)

Book (Paperback)

ISBN-13 (15)

978-93-7185-705-5

How to Cite

Davuluri, P. S. L. N. . (2026). The Autonomous Data Enterprise: Engineering Real-Time Intelligence with Generative and Agentic AI. Deep Science Publishing. https://doi.org/10.70593/978-93-7185-592-1
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