This page shows the most relevant public items for AI Engineering, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Mar 4, 2026 • arXiv • G. Madan Mohan, Veena Kiran Nambiar, Kiranmayee Janardhan
As autonomous systems proliferate, standard capability benchmarks fail to evaluate the nuanced behavioral governance required for enterprise deployment. This research introduces Design Behaviour Co...
Paper • Mar 4, 2026 • arXiv • Jiawei Chen, Tianzhuo Yang, Guoxi Zhang, Jiaming Ji, Yaodong Yang, Juntao Dai
Aligning large language models to individual user values without compromising the core safety parameters of the foundation model is notoriously difficult. This paper introduces VISA, a shielded ada...
Paper • Mar 4, 2026 • arXiv • Ratna Kandala, Niva Manchanda, Akshata Kishore Moharir, Ananth Kandala
Detecting deceptive or manipulative intent across long-term interactions is a major blind spot for standard stateless LLMs. The authors introduce EchoGuard, an agentic framework that utilizes a per...
Book • Oct 15, 2023 • Amazon • Sinan Ozdemir
Designed for both developers and technical product managers, this guide offers a rapid, comprehensive onboarding to the world of large language models. Sinan Ozdemir breaks down the core components...
Paper • Jan 19, 2026 • arxiv.org • Chen et al.
Despite recent advancements in Large Language Models (LLMs), complex Software Engineering (SE) tasks require more collaborative and specialized approaches. This concept paper systematically reviews...
Blog • Dec 22, 2025 • The New Stack • Richard MacManus
This article analyzes key AI engineering trends in 2025, highlighting the rise of vibe coding alongside agentic AI systems. It explains how developers are shifting from writing code to orchestratin...
Blog • Dec 11, 2025 • Towards Data Science • Keshav Dhandhania
This article explores orchestration techniques used to coordinate AI agents and tools in complex workflows. It explains how planners, controllers, and execution layers interact within agent archite...
Book • Aug 31, 2023 • Amazon • Olivier Caelen, Marie-Alice Blete
This O'Reilly guide serves as a foundational text for developers looking to integrate the power of OpenAI's models into their software stack. It provides a comprehensive overview of the OpenAI API,...
Book • Dec 22, 2023 • Amazon • Ben Auffarth
LangChain has emerged as the premier orchestration framework for connecting large language models to external tools and databases. This practical guide walks developers through the process of build...
Book • Oct 22, 2024 • Amazon • Paul Iusztin, Maxime Labonne
Moving an LLM project from a messy Jupyter notebook into a robust production environment is the primary challenge for AI teams today. This handbook offers a comprehensive roadmap for designing, tra...
Paper • Jan 27, 2026 • arxiv.org • Débora Souza, Patrícia Machado
LLM-based agents are becoming central to software engineering tasks, yet evaluating them remains fragmented and largely model-centric. Existing studies overlook how architectural components, such a...
Paper • Jan 27, 2026 • arxiv.org • Yuheng Tang, Kaijie Zhu, Bonan Ruan, Chuqi Zhang, Michael Yang, Hongwei Li, Suyue Guo, Tianneng Shi, Zekun Li, Christopher Kruegel, Giovanni Vigna, Dawn Song, William Yang Wang, Lun Wang, Yangruibo Ding, Zhenkai Liang, Wenbo Guo
Even though demonstrating extraordinary capabilities in code generation and software issue resolving, AI agents' capabilities in the full software DevOps cycle are still unknown. Different from pur...
Paper • Jan 27, 2026 • arxiv.org • Shamse Tasnim Cynthia, Joy Krishan Das, Banani Roy
Autonomous AI agents are transforming software development and redefining how developers collaborate with AI. Prior research shows that the adoption and use of AI-powered tools differ between core ...
Paper • Jan 28, 2026 • arxiv.org • Kazuma Yamasaki, Joseph Ayobami Joshua, Tasha Settewong, Mahmoud Alfadel, Kazumasa Shimari, Kenichi Matsumoto
As software engineering moves toward SE3.0, AI agents are increasingly used to carry out development tasks and contribute changes to software projects. It is therefore important to understand the e...
Paper • Jan 28, 2026 • arxiv.org • Ling Tang, Jilin Mei, Dongrui Liu, Chen Qian, Dawei Cheng, Jing Shao, Xia Hu
Large language model-powered multi-agent systems have emerged as powerful tools for simulating complex human-like systems. The interactions within these systems often lead to extreme events whose o...
Paper • Jan 28, 2026 • arxiv.org • Jonathan Rystrøm, Chris Schmitz, Karolina Korgul, Jan Batzner, Chris Russell
Deploying Large Language Model-based agents (LLM agents) in the public sector requires assuring that they meet the stringent legal, procedural, and structural requirements of public-sector institut...
Paper • Jan 28, 2026 • arxiv.org • S M Mahedy Hasan, Md Fazle Rabbi, Minhaz Zibran
We present the first empirical study of AI-generated pull requests that are 'silent,' meaning no comments or discussions accompany them. This absence of any comments or discussions associated with ...
Paper • Jan 29, 2026 • arxiv.org • Haoming Huang, Pongchai Jaisri, Shota Shimizu, Lingfeng Chen, Sota Nakashima, Gema Rodríguez-Pérez
Large Language Model (LLM) Agents are advancing quickly, with the increasing leveraging of LLM Agents to assist in development tasks such as code generation. While LLM Agents accelerate code genera...
Paper • Jan 29, 2026 • arxiv.org • Johannes Kirmayr, Lukas Stappen, Elisabeth André
Existing benchmarks for Large Language Model (LLM) agents focus on task completion under idealistic settings but overlook reliability in real-world, user-facing applications. In domains, such as in...
Paper • Jan 29, 2026 • arxiv.org • Evelien Riddell, James Riddell, Gengyi Sun, Michał Antkiewicz, Krzysztof Czarnecki
Root cause analysis (RCA) is essential for diagnosing failures within complex software systems to ensure system reliability. The highly distributed and interdependent nature of modern cloud-based s...
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