Choosing an Agentic AI Framework: A Practical Guide for Engineering Leaders

This article provides a practical guide for engineering leaders choosing an agentic AI framework, focusing on LangGraph, Spring AI, and Strands. It argues that while framework selection is important (around 20% of success), it's overshadowed by prompt engineering (40%), tool design (20%), and evaluation strategy (15%). The author emphasizes prioritizing speed to market and choosing a framework that offers provider/vector database flexibility and robust observability for production debugging, rather than chasing the "best" framework overall.