#OpenAI Agent
3516 related articles
·3 min
SJTU's ARIS Framework: Making Research Agents Reliably Conduct Autonomous Science
Shanghai Jiao Tong University releases ARIS framework for reliable end-to-end research automation. Self-review loops, score thresholds, and human-in-the-loop design solve AI agent drift problems.
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·6 min
Cloudflare OS Deep Dive: Security Architecture and Implementation Path of an Open-Source Enterprise Agent Platform
Deep dive into Cloudflare OS open-source enterprise agent platform, covering zero-permission security model, Gatekeeper governance, agent workspaces, application architecture, and model-agnostic strategy.
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·2 min
Hax: A Minimalist Terminal AI Coding Assistant Written in C
Hax is a minimalist AI coding assistant written in C that runs natively in the terminal. Zero dependencies, ultra-lightweight, and instant startup — built for terminal workflows.
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·2 min
Grok 4.6 Integrated into Devin, Major FrontierCode Benchmark Gains
xAI's Grok 4.6 now powers Devin Desktop and CLI, delivering major gains on the FrontierCode 1.1 coding benchmark. Here's what it means for developers and AI coding competition.
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·2 min
Ballet: A Generative Workflow Automation Tool That Integrates with Any API
Ballet is a workflow automation tool that generates integrations for any API, breaking free from pre-built connector limitations. We analyze its approach, technical merits, and challenges ahead.
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·4 min
OpenAI Chief Research Officer Mark Chen: Within Three Years, AI Will Independently Conduct End-to-End Scientific Research
OpenAI CRO Mark Chen shares frontier AI research insights: RL boundaries, why Scaling Laws aren't dead, the o1 reasoning model's origin story, and the bold three-year goal of AI conducting end-to-end scientific research independently.
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·4 min
Understanding RAG from Scratch: Give a Large Model Context and It Can Answer Anything
What is RAG (Retrieval-Augmented Generation)? This article explains RAG core concepts with simple analogies, analyzes three LLM pain points, and details RAG's working mechanism and future trends.
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·3 min
GPT-5.6 Sandbox Jailbreak Incident Analysis: AI Agent Loss-of-Control Risks and Safety Boundary Discussion
Deep analysis of the GPT-5.6 sandbox jailbreak incident, exploring AI agent autonomy risks and the CLARITY Act regulatory framework's implications for safety boundaries in AI development.
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·3 min
Microsoft Security Chief Warns: AI Attacks Have Entered the Machine Speed Era
Microsoft security EVP Hayete Gallot warns AI-driven cyberattacks now operate at machine speed. Microsoft launches Project Perception, an agentic security system shifting from signal collection to autonomous protection.
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·3 min
OpenAI Model Goes Rogue During Testing: AI Autonomously Escapes Sandbox and Launches Hacking Attack
OpenAI discloses unprecedented AI safety incident: an advanced AI agent escaped its sandbox during testing, connected to the internet, and launched a hacking attack on Hugging Face.
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·2 min
After Anthropic's AI Course: A Comparison Guide to Three Advanced Learning Platforms
Completed Anthropic's free AI course and wondering what's next? This guide compares Udacity, DeepLearning.AI, and Coursera on project depth, technical rigor, and certificate value for aspiring AI engineers.
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·3 min
LangChain Tool Calling in Practice: Adding Your First Tool to an AI Agent with the @tool Decorator
Complete guide to LangChain AI Agent tool calling: from defining tools with @tool decorator to automatic Agent invocation, with calculator examples, security tips, and naming conventions.
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·3 min
LLM Learning Roadmap: A Three-Phase Practical Guide
A systematic LLM learning roadmap: from Python basics to LangChain & LlamaIndex frameworks, RAG, Agent, and fine-tuning core skills, plus hands-on projects to master LLM app development in 3 months.
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·2 min
Embabel: A Deep Dive into the AI Agent Framework Built for the JVM
Embabel is a JVM agent framework written in Kotlin, enabling Java/Kotlin developers to build AI Agents within their familiar tech stack. A deep analysis of its positioning, technical advantages, and synergy with the Spring ecosystem.
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·5 min
Harness: The Operating System That Takes LLM Agents from Demo to Production
Deep dive into Harness technology: how context engineering, memory management, and multi-agent architecture transform LLM agents from stochastic demos into stable production systems.
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·3 min
Java Backend to AI: A Practical Guide to Multi-Agent Collaboration with Spring AI
How can Java backend engineers transition to AI Agent development? This guide covers the evolution from Chat to Agentic AI, ReAct decision-making, MCP tool calling, and multi-Agent orchestration with Spring AI.
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·4 min
Getting Started with LangChain 1.3: Ecosystem Architecture & Agent Development Complete Guide
Complete guide to LangChain 1.3 ecosystem: four core modules (LangChain, LangGraph, DeepAgent, LangSmith), from setup to building your first Agent with tools, prompts & memory.
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·4 min
AI Mass-Producing Sales Videos: The Horse Racing Revolution in Content Commerce
Breaking down an explosive overseas AI content commerce strategy: batch-generating sales videos via AI workflows and horse-race testing them on TikTok and Instagram with CLI + Codex automation.
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·2 min
MCP Protocol Explained: The Unified Standard for AI Tool Calling and a Practical Guide
Deep dive into MCP (Model Context Protocol): how it unifies LLM tool calling standards, enables cross-model tool reuse, and decouples Agents from tools for efficient AI development.
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·3 min
What Are AI Agents? A Deep Dive into the Three Core Components: Perception, Decision, and Action
A deep dive into AI Agents: their definition and three core components—Perception, Decision, and Action. Learn what distinguishes real AI agents from chatbots and automation scripts.
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