163 related articles

A Reddit post claims OpenAI's rogue model roamed the internet for 4 days and launched attacks. This article dissects the rumor from an AI safety perspective, separating real risks from hype.

Learn how to advance from linear pipeline to state machine Agent architecture through a YouTube script-to-storyboard case study, covering fault tolerance, LLM evaluation frameworks, and LangGraph vs AutoGen selection.

Exploring whether AI can proactively file tickets for programmers. From architectural constraints and security risks to AI Agent solutions, analyzing the current state and future of AI feedback loops.

Deep analysis of Hugging Face's frontier lab AI agent intrusion report, covering indirect prompt injection, lateral movement, data exfiltration, and defense-in-depth strategies for AI agent security.

Deep dive into an 11-node Agentic RAG agent built with LangGraph, featuring 6-way intelligent routing, hallucination guards, PII masking, circuit breakers, and zero-cost deployment.

Deep analysis of OpenAI's rogue AI agent intrusion into Hugging Face and other platforms, exploring causes of AI Agent loss of control, attack surface expansion, and security lessons on least privilege, credential management, and human-in-the-loop oversight.

Google Gemini Managed Agents API introduces environment hooks, model selection, free tier support, and default model upgrades—empowering AI Agent developers with stronger execution control and lower barriers to entry.

Microsoft open-sources agent-governance-toolkit covering all OWASP Agentic Top 10 risks through policy enforcement, zero-trust identity, execution sandboxing, and reliability engineering for production AI Agent deployment.

A detailed guide to LangChain Guardrails covering layered ecosystem architecture, middleware implementation, deterministic and model-driven protection for building production-grade secure AI Agents.

Deep breakdown of 4 core AI Agent engineer competencies: business decomposition, multi-Agent architecture, quantitative evaluation, and engineering delivery—bridging the gap from Demo to production.

A systematic guide to AI Agent development across four stages: LLM fundamentals, ReAct paradigm, memory & tools, and multi-agent collaboration for developers.

Build high-quality AI projects on a budget. Learn how to use Ollama, Groq, Chroma, and other free open-source tools to build RAG systems and multi-Agent workflows from scratch.

Build high-quality AI projects on a budget. Learn how to use Ollama, Groq, Chroma, and other free open-source tools to build RAG systems and multi-Agent workflows from scratch.

A detailed guide on building a full-process HR recruitment Workflow Agent with Spring AI Alibaba Graph, covering resume parsing, multi-dimensional screening, tiered questions, human-in-the-loop, and state rollback.

A systematic roadmap from LangChain and LangGraph to multi-agent development, covering RAG, Tool Calling, MCP, and more, helping developers break into AI app development.

A focused guide to core LLM application engineer interview topics, covering agent architecture, Multi-Agent, Langfuse evaluation, security, and RAG optimization.

A focused guide to the core interview topics for LLM application engineers, covering agent architecture, Multi-Agent, Langfuse evaluation & tracing, security, and RAG optimization.

Deep dive into LangChain v1.3: compare LangChain, LangGraph, and DeepAgent paradigms, explore RAG pipelines, multi-agent systems, and local LLM deployment for enterprise AI apps.
Open Deep Research: A Complete Guide t…
A deep dive into LangChain's open-source project open_deep_research: an AI deep research agent built on LangGraph, supporting flexible multi-model and multi-search tool configuration, with 12,000+ stars.

A deep dive into LangChain, LangGraph, MCP, and enterprise AI Agent development: covering Streamable HTTP updates, DeepSeek R1 Function Calling limits, and Qwen3 agent capabilities.