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.
Blog • Feb 8, 2026 • Medium • Mark S. Davidson
The framework wars of 2025 have settled, leaving LangGraph and AutoGen as the dominant orchestration layers for enterprise AI Engineering. Davidson provides a brutal, battle-tested comparison of th...
Paper • Feb 5, 2026 • arXiv • Minghao Du, Yifan Sun, Zeyu Li
A-RAG proposes an agentic retrieval framework that exposes retrieval tools directly to large language models. Rather than treating retrieval as a static preprocessing step, the architecture allows ...
Paper • Feb 11, 2026 • arXiv • Pengcheng Zhou, Haochen Li, Zhiqiang Nie, JiaLe Chen, Qing Gong, Weizhen Zhang, Chun Yu
This paper introduces CogitoRAG, a cognitive-inspired retrieval-augmented generation framework designed to mimic human episodic memory and semantic gist formation. The system constructs a multi-dim...
Blog • Dec 30, 2025 • OpenAI Developers • OpenAI
This developer-focused post summarizes the major platform changes enabling production-grade AI systems in 2025. It highlights new APIs, models, and tooling designed to support autonomous agents and...
Blog • Oct 6, 2025 • OpenAI • OpenAI
This post introduces AgentKit, a toolkit designed to simplify the development and deployment of AI agents. The article explains how fragmented tooling previously made building agentic systems compl...
Blog • Feb 20, 2026 • Medium • ZeroDay_Scribe
As Agentic AI systems gain persistent memory and direct API access, traditional prompt injection defenses are no longer sufficient. This highly technical post introduces the concept of 'State Poiso...
Blog • Mar 5, 2026 • Medium • Marcus Sterling
For the past year, web automation agents have relied heavily on parsing HTML DOM structures, making them notoriously brittle whenever a website updates its layout. The release of OpenClaw 3.0 this ...
Paper • Feb 28, 2026 • arXiv • Diego Gosmar, Deborah A. Dahl
Defending autonomous systems from prompt injection remains a critical security hurdle. This paper introduces a multi-layered defense architecture utilizing a dedicated 'Security Agent' equipped wit...
Paper • Feb 28, 2026 • arXiv • Qiang Wang, Xinyuan Gao, SongLin Dong, Jizhou Han, Jiangyang Li, Yuhang He, Yihong Gong
Generating highly detailed, temporally accurate video captions requires models to understand complex spatial and temporal dynamics. VDC-Agent introduces an autonomous framework where the captioning...
Paper • Feb 28, 2026 • arXiv • Ziheng Geng, Jiachen Liu, Ran Cao
Predicting complex human economic behavior is notoriously difficult using traditional statistical models. InsurAgent leverages LLM-empowered agents to simulate thousands of distinct individual pers...
Paper • Mar 4, 2026 • arXiv • Yong Liu, Xingjian Su, Shiyu Wang, Haoran Zhang
While text and image foundation models have scaled massively, time-series forecasting has lagged behind due to architectural constraints. Timer-S1 introduces a billion-parameter foundation model sp...
Paper • Feb 28, 2026 • arXiv • Runquan Gui, Yafu Li, Xiaoye Qu, Ziyan Liu, Yeqiu Cheng, Yu Cheng
Large Language Models frequently produce correct final answers based on flawed or unfaithful intermediate reasoning steps. This paper proposes Step-Level Faithfulness Maximization, a training parad...
Paper • Feb 28, 2026 • arXiv • Jiangping Huang, Wenguang Ye, Weisong Sun, Jian Zhang, Mingyue Zhang, Yang Liu
While LLMs excel at generating initial code snippets, they struggle immensely with resolving complex runtime errors. TraceCoder introduces a multi-agent framework that utilizes actual execution tra...
Paper • Mar 4, 2026 • arXiv • Zhiming Xue, Yujue Wang
Predictive models in logistics often act as black boxes, making them difficult for supply chain managers to trust. This paper integrates Temporal Graph Attention Networks with an LLM-driven reasoni...
Paper • Mar 4, 2026 • arXiv • Minjune Hwang, Yigit Korkmaz, Daniel Seita, Erdem Bıyık
Reward learning from human preferences often suffers from spurious correlations, leading agents to develop brittle and misaligned behaviors. The authors present a framework that integrates causal i...
Book • Sep 30, 2024 • Amazon • Denis Rothman
RAG-Driven Generative AI provides a detailed roadmap for designing, managing, and controlling multimodal AI pipelines that balance high performance with API cost efficiency. It offers deep explorat...
Book • May 20, 2025 • Amazon • Arindam Ganguly
Integrating generative AI breakthroughs into large-scale, production-grade enterprise applications presents significant architectural challenges. This enterprise-focused book takes a pragmatic appr...
Book • Jul 11, 2025 • Amazon • Salvatore Raieli, Gabriele Iuculano
This comprehensive guide empowers developers to leverage retrieval-augmented generation (RAG), knowledge graphs, and agent-based architectures to engineer truly intelligent behavior. It provides a ...
Book • May 17, 2024 • Amazon • Valentina Alto
As Large Language Models evolve from experimental research into core enterprise technology, developers need a practical roadmap to build reliable products. This book focuses entirely on the applica...
Book • Jun 18, 2024 • Amazon • James Phoenix, Mike Taylor
While many books focus strictly on backend infrastructure, this guide targets the crucial skill of optimizing the actual inputs that drive foundation models. Phoenix and Taylor explore the science ...
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