A deep technical guide to designing, orchestrating, and scaling multi-agent systems using LLM-based agents, coordination protocols, and modern AI engineering patterns.
Notes tagged with ai-agents
ai-agents
A guide to deciphering model names for better AI Engineering decisions
Learn how to effectively control the behavior of Large Language Models (LLMs) using inference parameters like temperature, top-p, and more. This guide provides practical examples with Python and LangChain.
Learn how to create precise, structured prompts for AI agents using the CO-STAR framework, enhancing reliability and performance in LLM applications.
Exploring the StateAct pattern to enhance AI agents' robustness and long-term task management. Learn how to implement this pattern using LangGraph and Ollama, ensuring agents maintain focus and clarity in complex tasks.