#LangGraph deployment
350 related articles
·5 min
GPT-5.6 Released: Outperforms Rivals but Halted by Government—Cybersecurity Capabilities Trigger Regulatory Red Line
OpenAI releases GPT-5.6 (Sol/Terra/Luna), beating Anthropic on Terminal Bench at ~40% lower cost. But its cybersecurity capabilities hit danger thresholds, limiting access to trusted partners at government request.
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·5 min
GPT-5.6 Released: Hands-On Analysis of Programmatic Tool Calling and Autonomous Agents
GPT-5.6 is officially released with core upgrades including programmatic tool calling, autonomous subagent delegation, and higher token information density. A hands-on card game build reveals its Agentic power.
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·4 min
Four Major LangChain Updates: OpenWiki, Voice Agents, and Sub-Agent Orchestration Explained
LangChain releases four major updates: OpenWiki for auto-generating codebase docs, voice agent tutorials, Harbor evaluation integration, and deepagents programmable sub-agents.
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·6 min
AI Agent Development in Go: A Hands-On Guide to ByteDance's Eino Framework and Its Seven Core Capabilities
Build production-grade AI Agents with a pure Go stack using ByteDance's Eino framework. A deep dive into seven core capabilities: multi-Agent orchestration, long-task execution, command approval, RAG, MCP, Skills, and database reporting.
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·5 min
Getting Started with LangChain: LLM Invocation, Agent Practice, and Environment Setup Guide
Starting from the three limitations of LLMs, this guide systematically explains LangChain's core positioning, environment setup, API key prep, model init, and the message system. Learn init_chat_model and AIMessage/HumanMessage/SystemMessage.
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·5 min
From Large Models to AI Agents: A Complete Analysis of the Four Evolutionary Stages and Four Major Commercial Tracks
Prompt engineering and RAG can no longer meet enterprise digital transformation needs—AI Agents are the key. This article breaks down the four evolutionary stages of large model deployment and the four major Agent commercial tracks.
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·5 min
Ollama Raises $65M Series B: 85% of Fortune 500 Have Already Deployed Local LLMs
Local LLM tool Ollama closes a $65M Series B, bringing total funding to $88M. With 9M developers and 85% of Fortune 500 having deployed internally, this deep dive explores why enterprises embrace local LLMs: compliance, Agent cost savings, and open-source ecosystem.
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·3 min
Java Developers Pivoting to AI: Why the Agent Direction Is the Best Choice
How can experienced Java and backend developers pivot to AI? This deep-dive explains why the Agent direction is the best fit — skills transfer well, market demand is high, and the path from "using frameworks" to "understanding source code" is clear.
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·3 min
How to Choose a Practical LLM/RAG/AI Agent Course: Four Key Criteria
Learn how to pick the best LLM, RAG, and AI Agent courses. Discover 4 key criteria for hands-on AI learning and top resources for developers.
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·4 min
AI Agent Learning Path for Beginners: From Theory to Real-World Practice
A complete AI Agent learning roadmap covering BDI theory, core components (Perception/Planning/Execution), AutoGen multi-agent frameworks, and DeepSeek RAG projects for beginners.
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·7 min
Turning Any AI Agent into an Orchestration Hub: A Deep Dive into Multi-Agent Collaboration Architecture
An in-depth analysis of the "any Agent as an orchestrator" design philosophy, exploring the technical implementation of multi-Agent collaboration, context management, and workflow automation.
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·5 min
28 Enterprise-Grade AI Agent Projects: A Complete Guide That Even Beginners Can Deploy
A collection of 28 fully reproducible enterprise-grade AI Agent projects covering code debugging, financial analysis, customer service, and multi-agent collaboration—deployable even for beginners.
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·4 min
AI Agent Learning Roadmap: From Zero to Real-World Projects
A complete AI Agent learning roadmap covering agent principles, prompt engineering, RAG, multi-agent systems, and hands-on projects — from zero to real-world deployment.
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·4 min
AI Agent Development Deep Dive: Opportunities, Real Entry Barriers, and Learning Paths
A deep dive into AI Agent development: real architecture, entry barriers, and learning paths. From ReAct to multi-agent systems and LangChain — cut through the hype.
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·4 min
8-Step AI Agent Development Guide: From Zero to Production
Demo works but production fails? This guide covers the full AI Agent development path: when to use Agents, hand-writing ReAct loops, tool schemas, RAG, eval sets, and production fallback strategies.
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·4 min
Proactive AI Agent Monitoring: Stop Fighting Fires, Catch Bugs Before Your Customers Do
A real case study: team builds AI Agent "Oogway" to auto-patrol after every job, investigate anomalies, create tickets, and update a knowledge Wiki — catching bugs before customers do.
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·4 min
Spring AI Alibaba Graph in Practice: HR Recruitment Workflow Agent Design and Core Technical Analysis
Build an HR recruitment workflow Agent with Spring AI Alibaba Graph, covering resume parsing, job matching, tiered question generation, HITL checkpointing, and time travel state rollback across 20 core technical points.
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·6 min
Getting Started with Coze Agents: Core Concepts, Platform Positioning, and a Practical Implementation Guide
A systematic guide to Coze's core positioning, its differences from Dify/n8n, and its full capability system covering agents, workflows, and multi-agent modes—helping beginners get started fast.
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·5 min
Security Swarm Evaluation in Practice: Validating AI Security Tools with Real Vulnerabilities
A deep dive into Security Swarm's evaluation methodology: building test sets from real, recent vulnerabilities to avoid training data contamination and validate its ability to find more bugs at lower cost.
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·3 min
Kastor: A New Approach to Managing AI Agents with Declarative Specs
Kastor is an open-source project that brings IaC-style declarative specs to AI Agent management, inspired by Terraform — solving reproducibility, collaboration, and auditability challenges.
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