Understanding the Thought-Action-Observation loop.
The ReAct pattern (2022) remains the most fundamental building block of AI agent design. Its elegance: Thought, Action, Observation in a continuous feedback loop. Modern frameworks like LangGraph and OpenAI Agents SDK still use ReAct as their default. The key insight: separating reasoning from action lets the LLM course-correct based on real environmental feedback. Start with ReAct and only add complexity when simpler patterns are insufficient.
2026-06-047 min
Orchestrator-Workers vs Plan-and-Execute
A practical comparison of two powerful agent patterns.
Plan-and-Execute works best when the task structure is predictable. Orchestrator-Workers shines when tasks are dynamic. In Orchestrator-Workers, a central LLM dynamically decomposes tasks and delegates to workers. Anthropic recommends this for complex coding tasks. Choose Plan-and-Execute for structured research; Orchestrator-Workers for open-ended problem solving.
2026-05-286 min
Building Reliable Agent Loops: Lessons from Anthropic
Key takeaways from Anthropic Building Effective Agents guide.
Anthropic three core principles: maintain simplicity, prioritize transparency, and craft your agent-computer interface carefully. Start with the simplest solution and add complexity only when demonstrably needed. Show planning steps explicitly. Invest in tool documentation and testing. At LoopCraft, these principles are baked into every pattern template.
