Notes tagged with

ai-agents

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.

From solo problem-solvers to orchestrated teams. This guide explores the 'why' and 'how' of multi-agent architectures, demonstrating how a team of specialized AI agents can solve complex problems more effectively than a single agent ever could.

A deep dive into advanced agentic frameworks like Tree of Thoughts (ToT) and Language Agent Tree Search (LATS) that enable AI agents to plan, explore, and self-correct.

Go beyond basic agents. Discover how the Reflexion framework equips your AI agents with memory and the capacity to learn from their mistakes, enhancing their robustness and intelligence.