Open-Source AI Course Maps Three Agent Loop Architectures and Their Provider Cost Tradeoffs

A new analysis from Decoding AI's open-source course outlines three distinct patterns for implementing agent loops — ReAct-style tool-use loops, plan-and-execute pipelines, and reflection-based self-correction cycles — along with the provider economics that make each approach more or less viable depending on team constraints. The piece addresses a practical gap in agentic development guidance by connecting architectural choices to real cost implications, such as how frequently calling a frontier model versus a smaller local model changes both latency and billing. For developers designing multi-step autonomous systems, understanding the cost structure of each loop type is essential to building economically sustainable agents at scale. The open-source nature of the course material means teams can adapt and extend the frameworks described without licensing concerns. This kind of structured taxonomy of agent patterns is increasingly valuable as the space matures beyond simple chatbot wrappers into complex orchestration systems.