#LLM agent
2325 related articles
·3 min
Local LLM Agent Lagging? Lowering num_gpu Is the Key
Local LLM crashing in Agent frameworks? The issue may be num_gpu set too high. Learn what num_gpu really controls (GPU layer offloading, not GPU count) and how to tune it for stable Agent performance.
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·3 min
Agent Skill Routing: Engineering Trade-offs Between Retrieval and LLM, and the Two-Stage Layered Architecture
Deep dive into Agent skill routing: comparing pure model vs. pure retrieval approaches, with a detailed two-stage layered architecture balancing accuracy, latency, and cost.
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·3 min
From RAG to Agent Tuning: A Complete Guide to Deploying LLM Agents
A deep dive into LLM Agent frameworks covering RAG, Agent core components (tools, memory, planning), and Agent Tuning workflows with cost considerations for production deployment.
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·2 min
LLM Agent Long-Term Memory Security: A Six-Stage Attack Surface and Defense Framework
Deep analysis of LLM agent long-term memory security threats, covering persistence, statefulness, and propagation of memory poisoning, with a six-stage lifecycle defense framework.
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·1 min
AI Agents Rebuild the Entire Testing Workflow: A Paradigm Revolution from LLMs to Agents
An in-depth look at how AI Agents are disrupting traditional software testing: the core differences between LLMs and Agents, four capability dimensions (planning/memory/tools/skills), and how test engineers can achieve 10x efficiency gains in test case generation.
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·1 min
AI Agents Reshape the Testing Workflow: A Paradigm Revolution from LLMs to Agents
An in-depth look at how AI Agents disrupt traditional software testing: the core differences between LLMs and Agents, four capability dimensions (planning/memory/tools/skills), and how testers achieve 10x efficiency gains.
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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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·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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·6 min
The AI Builder's Handbook: A Complete Guide to LLM Principles, RAG Architecture, and Agent Design
From the autocomplete nature of LLMs, tokens, and context windows to RAG vector databases, the MCP protocol, and AI agent loop design — this article uses vivid analogies to unpack the reality of AI engineering.
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·2 min
AI Agents 101: The Core Difference Between LLMs and Agents, and How They're Built
What is an AI agent? How does it differ from a large language model? Learn the core concepts, the Agent formula (LLM + Workflow + Knowledge Base), and how to choose between Dify, LangChain, and LlamaIndex.
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·2 min
GPT-5.6 Deep Dive: Tiered Models + Multi-Agent Architecture, LLMs Evolve into Workflow Engines
GPT-5.6 isn't just a capability upgrade — it's an architectural shift: tiered models, programmatic tool calling, and multi-agent collaboration turn LLMs into workflow engines for production systems.
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·3 min
From LLMs to Agents: Understanding the Core Logic and Use Cases of AI Agents
LLMs answer questions; Agents actually get things done. This article breaks down the differences between LLMs, Chatbots, and Agents, explains the perceive–think–act architecture, and maps real-world use cases across education, finance, and healthcare.
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·3 min
LangChain 1.3 in Practice: A Complete Learning Path from LLMs to Agent Development
A comprehensive guide to LangChain 1.3 — covering the full learning path from Models to Agent development, including Harness architecture, LangGraph, memory management, HITL, and Guardrails.
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·3 min
AI Agent Beginner's Guide: LLMs vs. Agents, and a Full Breakdown of Development Tools
What is an AI Agent? This guide explains the key differences between LLMs and Agents, breaks down the Agent formula (LLM + Workflow + Knowledge Base), and compares tools like Dify, Coze, LangChain, and LlamaIndex.
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·1 min
Agentic Coding in Practice: Test Feedback Loops, LLM Benchmarks, and an Engineering Adoption Guide
An in-depth look at agentic coding: how test-driven loops enable AI self-correction, the real limits of LLM benchmarks, and key engineering lessons on context management and human-AI collaboration.
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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
Getting Started with LangChain: LLM Limitations, Agent Principles, and Environment Setup in Practice
An in-depth look at LangChain's core value: the three limitations of LLMs, unified model interfaces, modular architecture, configuring the DeepSeek API, and understanding the SystemMessage/HumanMessage/AIMessage/ToolMessage system to build a foundation for Agent development.
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·2 min
4-Step Roadmap to Becoming an AI Agent Engineer: From Zero to a $50K LLM Role
A proven 4-step roadmap to becoming an AI Agent engineer: stable LLM calls, tool use (RAG + Function Calling), production engineering, and resume optimization.
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·4 min
Building an LLM Wiki with Agents: Let AI Automatically Manage Your Knowledge Base
A deep dive into the LLM Wiki: how Agents auto-build indexes and bidirectional links to solve slow, Token-heavy retrieval in growing knowledge bases. Full breakdown of its three-layer structure.
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