#LLM-based agent
127 related articles
·3 min
Graph Engineering: The Core Paradigm and Design Patterns for AI Agent Orchestration
Deep dive into how graph engineering uses state machines and directed graphs to constrain AI agent behavior, covering reflection, routing, human-in-the-loop, and parallel execution patterns.
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·3 min
Graph Engineering: The Core Paradigm and Design Patterns for AI Agent Orchestration
Explore how graph engineering uses state machines and directed graph structures to constrain AI agent behavior, covering reflection, routing, human-in-the-loop, and parallel execution patterns.
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·3 min
Three Words to Break Claude Opus 5? Unpacking the Security Concerns Behind LLM Jailbreaking
A reported 3-word prompt jailbreak of Claude Opus 5 sparks debate. We analyze the technical nature of LLM jailbreaks, alignment fragility, and defense-in-depth strategies for enterprise AI security.
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·3 min
Three Words to Break Claude Opus 5? Unpacking the Security Concerns Behind LLM Jailbreaking
A reported 3-word jailbreak of Claude Opus 5 sparks debate. We analyze LLM jailbreak mechanics, alignment fragility, and defense-in-depth strategies for AI security.
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·2 min
AI Builds the Largest Open-World MMO in History: A New Paradigm for Game Development
Exploring how AI drives large-scale MMO development, from scalable content generation to dynamic NPC interaction, analyzing technical pathways, challenges, and industry implications.
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·5 min
Learn AI Agent Development from Scratch: A 3-Step Practical Path to Get Started Quickly
How to learn AI Agent development from scratch? This article outlines a clear 3-step path: Python crash course, LLM theory & practice, and LangChain framework project implementation.
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·4 min
A Beginner's Guide to AI Agents: Core Principles and Learning Paths Explained
Learn AI Agent core principles from scratch: understand how Agents differ from LLMs, their execution mechanisms, why rule design matters, and find the right learning path for your goals.
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·3 min
Unified Prompt Management, Evaluation & Observability: A Comprehensive Comparison of LangSmith and Other Leading Platforms
In-depth comparison of LangSmith, Langfuse, PromptLayer, Helicone, and Orq.ai across Prompt management, Evals, and observability to help teams choose the best unified LLM Ops platform.
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·3 min
API Mock vs. Sandbox in Agent Testing: A Detailed Layered Strategy Guide
API Mock is fast but misses bugs; Sandbox is realistic but costly. This article analyzes their core differences and provides a layered testing strategy for building reliable Agent test systems.
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·3 min
Spring AI Alibaba Graph in Practice: Building a Full-Process HR Recruitment Agent
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.
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·3 min
Zhipu's Mobile AI Agent Tested: A Full Breakdown of the Cloud Phone Approach — Capabilities and Limitations
Hands-on test of Zhipu's mobile AI Agent: using a cloud phone to bypass permission limits, it supports natural language-driven automation. We cover its core mechanics, real performance, app restrictions, and future potential.
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·2 min
The New Bar for AI/LLM Roles: A Practical Guide to Agent Observability and Evaluation
Enterprise AI/LLM roles now demand engineering skills: streaming recovery, high concurrency, multi-tenancy, LLM gateways, Langfuse observability, and evaluation platforms. Master these 8 core competencies.
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·6 min
Making Money with AI Coding Gigs: A Sober Breakdown and Honest Assessment of the Four-Week Learning Path
Can beginners really earn over 10,000 yuan in their first month with AI coding gigs? This article breaks down the four-week AI coding learning path week by week and objectively assesses the real monetization barriers.
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GitHub Daily · July 23: The Duet of Ru…
·3 min
GitHub Daily · July 23: The Duet of Rust's Dominance and the AI Agent Collaboration Paradigm
GitHub Trending July 23: block/buzz tops the chart with 3,252 stars, Rust dominates system tools, and AI Agents shift from tools to parallel collaborators.
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·7 min
The Complete Guide to LLM Application Engineer Interviews: Six High-Frequency Topics in the Agent Domain
A focused guide to the core interview topics for LLM application engineers, covering agent architecture, Multi-Agent, Langfuse evaluation & tracing, security, and RAG optimization.
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·7 min
The Complete Guide to LLM Application Engineer Interviews: Six High-Frequency Agent Topics
A focused guide to core LLM application engineer interview topics, covering agent architecture, Multi-Agent, Langfuse evaluation, security, and RAG optimization.
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·3 min
AI Agent Production Guide: Four Orchestration Patterns That Actually Work
Most AI agents never make it past the demo stage. This guide covers four production-grade agent patterns—workflow orchestration, policy-constrained execution, anomaly handling, and load routing—to help teams build reliable agent systems.
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·2 min
Dify × RAG for Beginners: Build Your Own Game AI Assistant from Scratch
Build an AI game assistant from scratch with no coding experience! This hands-on guide walks you through Dify + RAG — from knowledge base setup to agent creation and tuning.
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·3 min
LangGraph Production Template in Practice: A Complete Guide to Budget Control and Canary Routing
Deep dive into langgraph-agent-stack: per-run dollar budget control, canary traffic routing, Mock testing mode, and 800+ test cases to safely deploy AI Agents from demo to production.
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Claude Is Mr. Meeseeks: The Disposable…
·2 min
Claude Is Mr. Meeseeks: The Disposable Nature of AI Agents and Its Engineering Implications
Using Rick and Morty's Mr. Meeseeks to explain Claude and AI agents: stateless execution, task atomicity, and multi-agent recursive failure risks. A deep dive for developers building better AI workflows.
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