#LangGraph interrupt approval
25 related articles
·3 min
The Next Evolution of Coding Agents: Letting the Brain Escape the Sandbox
Exploring the next evolution in coding agent architecture: decoupling the reasoning core from code execution environments to separate control and execution planes.
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·2 min
From Workflow Graphs to Distributed Protocols: Dissecting Multi-Agent System Architecture Paradigms
How does Cosmonapse replace LangGraph's graph structure with event-driven distributed protocols? A deep dive into two multi-agent architecture paradigms, their trade-offs, and when to use each.
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·3 min
Cosmonapse: A Decentralized Multi-Agent Framework Built on an Event Bus
Cosmonapse is an open-source multi-agent framework that replaces central orchestrators with peer nodes, using typed signals and an event bus to fully decouple tool calls, memory, and HITL. Supports Python and TypeScript under Apache 2.0.
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·4 min
LangChain + LangGraph Full-Stack in Practice: A Production-Ready Guide to Six Core Modules and Agent Deployment
A systematic breakdown of LangChain's six core modules (Models/Prompts/Chains/Memory/RAG/Agent) and LangGraph's state graph, persistence, and HITL — with production deployment tips.
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·2 min
Impri: A Structural Human Approval Gateway for AI Agents
A deep dive into Impri — a structural human approval gateway for LangChain/LangGraph agents, exploring why prompt-level constraints fail and how code-layer gates enable reliable human-in-the-loop AI.
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·3 min
How to Test High-Risk Actions in AI Agents Before Going Live: A Complete Strategy Beyond Mocking
Mock testing can't cover the real side effects of high-risk, irreversible AI Agent actions. Learn sandbox environments, shadow mode, dry run, HITL, and more.
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·3 min
LangChain 1.3 in Practice: A Complete Technical Guide from LLMs to DeepAgent
An in-depth analysis of the core knowledge system of LangChain 1.3, covering the Harness architecture philosophy, DeepAgent positioning, LangGraph fundamentals, Agent memory, and human-in-the-loop.
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·4 min
A Complete Breakdown of the Engineering Challenges Behind RAG and Agent Interview Questions
Resume full of RAG and Agent but keep failing interviews? The issue is you only run demos and can't explain production engineering challenges. This article breaks down data cleaning, hybrid retrieval, hallucination protection, and agent loop breakers.
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·6 min
GPT-5.6 Now Widely Available: The Era of Three-Tier Models and Multi-Agent Orchestration Arrives
OpenAI releases GPT-5.6 (SOUL/TERRA/LUNA), with Ultra mode running four agents in parallel; Meta launches Muse Spark 1.1 with million-token context; ChatGPT desktop unifies Chat, Work, and Codex.
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·4 min
Kun: An Open-Source AI Coding Agent Deeply Optimized for DeepSeek
Kun is an open-source AI coding agent optimized for DeepSeek and domestic users, with nearly 5,000 GitHub stars. Features include requirements drafting, inline diffs, cost visualization, and mobile monitoring. Real-world cache hit rates reached 97%, keeping costs extremely low.
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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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·6 min
LangChain 1.3 in Practice: A Complete Guide to DeepAgent Architecture and the Harness Philosophy
An in-depth look at LangChain 1.3's core modules and DeepAgent architecture—covering the Harness philosophy, LangGraph internals, HITL, memory management, and guardrails to master production-grade AI Agent development.
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·3 min
4 Core Competencies Every High-Value LLM Application Engineer Must Have
Why can't companies find qualified AI engineers? Discover the 4 core competencies every high-value LLM application engineer needs: task decomposition, tool calling, observability, and production readiness.
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·7 min
Agentic AI Development in Practice: Evaluation and Error Analysis Are the Real Competitive Edge
Cut through the Agentic AI hype to see the real value of agentic applications. Based on Andrew Ng's course, learn why Evals and error analysis—not framework choice—separate top developers.
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Enterprise AI Factory: Governance Fram…
·3 min
Enterprise AI Factory: Governance Frameworks and Practical Guides for Autonomous AI Agents
Explore how enterprises building AI Factories can govern autonomous AI agents through identity management, runtime protection, and defense-in-depth to balance autonomy with security.
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·7 min
Andrew Ng Teams Up with LangChain: Core Principles of LangGraph Agent Development
Andrew Ng and LangChain CEO Harrison Chase present AI Agents in LangGraph, covering five core agent design patterns and LangGraph's graph-based framework for building cyclical agentic workflows.
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·7 min
Andrew Ng Teams Up with LangChain: Core Concepts of LangGraph Agent Development
Andrew Ng and LangChain CEO Harrison Chase's AI Agents in LangGraph course covers five agent design patterns and LangGraph's graph-based framework for building cyclical AI workflows.
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·6 min
Getting Started with LangChain: A Deep Dive into Model and Agent Core Concepts
Deep dive into LangChain's core Model and Agent concepts, covering unified model interfaces, agent tool calling, middleware mechanisms, and key principles for building LLM applications.
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·2 min
What Is AI Loop? The Paradigm Shift from Agents to Loop-Based Architecture
Deep dive into AI Loop architecture: how continuous-running agent swarms differ from traditional AI Agents, with applications in software development, cybersecurity, and beyond.
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·4 min
A Practical Guide to Loop Engineering: Pain Points and Solutions in Automated AI Coding Workflows
Deep dive into Loop Engineering: core mechanisms, three major pain points (reliability, cost, context bloat), and Harness workflow solutions including mixed model strategies and Human in the Loop.
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