May 9, 2026 · Artificial Intelligence

Agentic RAG Deep Dive: Letting the Agent Decide When and How Often to Retrieve

The article analyzes the shortcomings of traditional one‑shot RAG pipelines, introduces four Agentic RAG patterns that let an LLM‑driven agent control retrieval strategy, source selection, query rewriting and retry limits, and provides concrete TypeScript implementations with LangGraph, code snippets, and practical pitfalls.

Agentic RAGLLMLangGraph

0 likes · 16 min read

Agentic RAG Deep Dive: Letting the Agent Decide When and How Often to Retrieve

Wu Shixiong's Large Model Academy

Nov 18, 2025 · Artificial Intelligence

How to Make LLM Agents’ Function Calls Stable and Accurate: 5 Proven Strategies

This article breaks down why function‑call reliability is the biggest bottleneck for LLM agents and presents a systematic five‑step loop—schema quality, prompt context, sampling, training data, and runtime defenses—plus concrete optimization techniques such as dynamic tool routing, plan‑execute, validation layers, memory injection, and log‑driven tuning, illustrated with real‑world cases.

AgentLLMTool Routing

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How to Make LLM Agents’ Function Calls Stable and Accurate: 5 Proven Strategies