[Advanced Guide] Building an Action-Taking LLM Agent with the ReAct Pattern Combining Tool Calling and Memory
This guide covers how to go beyond a simple chatbot and build a real agent with external API calls and long-term memory. It walks through how the ReAct pattern works and through fully automated, hands-on code examples that combine Tool Call
Beyond Prompts: An In-Depth Guide to Production-Level Autonomous Agent Workflow Design
Simple prompt instructions cannot handle complex business logic. This guide dives into the architectural principles for building reliable autonomous agent systems through the ReAct pattern, systematic Tool Calling, and Multi-Agent System (M
[Developer Guide] Mastering LLM Agents: From Concepts to Architecture Design
Curious about how AI agents that actually execute work, beyond simple chatbots? This guide gives a developer-focused deep dive into agent internals—from the ReAct pattern and Tool Calling architecture to the Planner–Memory–Executor design.
Designing Multi-Step Reasoning for LLM Agents with the ReAct Pattern
A guide to the reasoning mechanisms that go beyond simple prompt calls—breaking down and solving complex problems step by step. Master the core principles of autonomous agent design in code, from the ReAct pattern to real Tool Calling imple
LLM Prompt Patterns: When to Use CoT, ToT, and ReAct
Part 1’s Zero-shot, Few-shot, Role, Chain, and Format techniques work well for simple Q&A, but production tasks that need complex reasoning and multi-step planning require more sophisticated patterns. This post covers Chain-of-Thought, Tree