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2,145 papers
This study delves into the notion of AI agentic architectures for autonomous data engineering pipelines and investigates the potential benefits of intelligent agents in enhancing automation, resilience, and decision-making processes in contemporary data ecosystems.
The paper provides a comprehensive analytical tool to make sense of micro and macro evidence, unpacks scenarios when AI agents will drive inclusive productivity growth, and outlines a policy roadmap focused on complementary investments, incentives for task-redesign, and workforce transition support measures.
It is concluded that future research should prioritize interdisciplinary collaboration, robust regulatory frameworks, and continuous monitoring to promote the ethical use of AI.
Policy drift takes shape through a nonlinear differential equation - framed within the policy state space - with support from Lyapunov stability concepts alongside bifurcation methods alongside bifurcation methods, and the Intent Drift Rate appears: a concrete number per dialogue turn built as the time-based change in...
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This conceptual paper theorizes agentic workflows systems, where an AI agent or agents proactively perceive, plan, act and reflect throughout the entire software development lifecycle (SDLC); its implications for end to end software engineering automation are discussed.
AI-driven cybersecurity in the software engineering field is discussed, where machine learning, deep learning, natural language processing, and reinforcement learning can be applied throughout the software development lifecycle to provide increased security.
Intent-Based Security (IBS), a structured approach built on foundational ideas from access control, zero-trust models, and principal-agent dynamics, shows why trusting identities fails against invisible threats.
A new scientific object – the Autonomous Recovery Efficiency Score (ARES) – is introduced – a quantitative measure of autonomous resilience, as well as a supporting foundation for future autonomous self-healing AI agentic infrastructure.
The speed of cloud computing and artificial intelligence, which have transformed the way software applications are designed and deployed. The cloud-based AI systems provide a scalable, adaptable, and cost-efficient solution to build intelligent systems capable of processing large amounts of data and running complicated...
Autonomous multi-agent artificial intelligence (AI) systems have emerged as a rapidly evolving field that revolutionizes the way autonomous systems can make decisions together, collaborate on tasks, and learn, thereby opening new paradigms for distributed decision-making, task execution, and adaptive learning. The comp...
Explainability in Large Language Model (LLM)–assisted PLC programming is essential for industrial adoption, where engineers must understand, validate, and maintain generated control logic under strict safety and standardization constraints. Existing explainability-oriented prompting approaches such as Chain of Note (Co...
Generating PLC programs with Large Language Models (LLMs) that are directly deployable in industrial toolchains remains challenging because outputs must satisfy IEC 61131-3 semantics and vendor-specific compilation constraints. Verification-centric prompting such as Chain-of-Verification (CoVe) can reduce reasoning err...
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
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