#stateful agent
198 related articles
Production-Grade LangGraph Template: A…
·2 min
Production-Grade LangGraph Template: A Practical Guide to Engineering AI Agents for Production
A deep dive into production-grade LangGraph templates covering state management, observability, error handling, and containerized deployment to bridge the gap from demo to production.
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·2 min
Spring AI in Action: A Complete Guide to LLM Integration for Java Developers
Spring AI is Java's answer to LangChain — offering unified multi-model APIs, structured output, RAG, Tool Calling, and MCP protocol support for enterprise LLM development.
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·3 min
Can Small Local Models Become Software Domain Experts? A RAG Architecture Deep Dive
Can small local models (1.5B–3B) become software domain experts? This article breaks down CPT, SFT, RAG, and Agent architectures, with a layered RAG-centric design for CPU-only local deployment.
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·6 min
A Deep Dive into OpenAI Codex: How AI Engineers Are Reimagining Software Development with Agents
OpenAI Codex is redefining how AI engineers work: from code completion to autonomous Agents, from single-threaded to parallel Value Maxing. A deep dive into the Codex App architecture, open ecosystem, and Manager of Agents practice.
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·3 min
Complete AI Agent Development Learning Path: From Core Principles to Enterprise Deployment
A structured AI Agent learning path covering core principles, prompt engineering, tool use, multi-agent systems, and frameworks like LangChain, CrewAI, and Dify for enterprise deployment.
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·2 min
LangChain Chain & Memory: Building AI Applications with Persistent Memory
Learn how LangChain's Chain and Memory components overcome LLM limitations. Build intelligent AI apps with multi-step workflows and persistent memory.
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·4 min
LangChain in Practice: Mastering the Four Core Modules of the Agent Ecosystem
A deep dive into LangChain's four core modules: LangChain components, LangGraph orchestration, Deep Agents, and LangSmith. Build your first Agent from scratch.
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·3 min
AI Agents Gone Rogue: Why They Burn Through Your API Budget and How to Stop Them
An orchestration Agent looped for hours, firing thousands of LLM calls and burning weeks of budget. Learn the root causes and practical defenses: circuit breakers, tiered budgets, and iteration limits.
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There's No Best Agent Framework — Only…
·2 min
There's No Best Agent Framework — Only the Right One for Your Needs: A Deep Comparison of Leading Options
LangGraph, PydanticAI, OpenAI Agents SDK, CrewAI — a senior developer's practical guide to choosing the right AI Agent framework for your project.
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Latent Reasoning: The Next-Generation …
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Latent Reasoning: The Next-Generation AI Reasoning Paradigm Beyond Chain of Thought
Is CoT really AI 'thinking'? This deep dive covers latent reasoning's rise — Coconut, HRM, BDH — and the core trade-offs between interpretability, efficiency, and governance in high-stakes 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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·2 min
State Machine Voice Agents in Practice: Balancing Latency and Accuracy with Pipecat and Vapi
A deep dive into state machine-based voice AI agent architecture, comparing Pipecat Flows and Vapi Squad, and exploring the latency vs. accuracy trade-offs in agentic handoffs.
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·3 min
Decoding Databricks' AI Strategy: The Omnigents Orchestration Platform and LTAP Unified Storage Architecture
An in-depth look at the AI strategy of Databricks co-founders Matei Zaharia and Reynold Xin: the open-source Agent platform Omnigents, the unified storage architecture LTAP, and how Dream Engine reshapes data and intelligence.
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·6 min
AI Agent Development Learning Path: Four Stages from Zero to Hands-On Projects
A systematic breakdown of the AI agent development learning path, covering four stages: fundamentals, RAG knowledge bases, tool use, multi-agent collaboration, and hands-on projects.
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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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·4 min
TigrimOSR: An Open-Source Agent Framework in Rust with Configurable Agent Loops
TigrimOSR is an open-source multi-agent system written in Rust, supporting full agent loop definition via YAML config files with only 250MB memory usage. A deep dive into Loop Engineering, Rust advantages, and self-hosted Agentic AI.
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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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·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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·7 min
Codex Direct Connection Configuration Guide for China: Complete GPT Agent Setup Tutorial
Complete guide for configuring OpenAI Codex Agent in China, covering installation, API key setup, permission modes, reasoning intensity, and security considerations for third-party relay services.
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·4 min
Tencent Open-Sources Agent Memory: A Fully Local AI Long-Term Memory Solution with Zero External Dependencies
Tencent Cloud open-sources TencentDB Agent Memory — a fully local AI Agent memory system with a 4-tier progressive pipeline, zero external API dependencies, and 8,100+ GitHub Stars. Ideal for finance, healthcare, and privacy-sensitive use cases.
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