Effective AI agents require more than just LLMs in loops—they need architectural patterns like late-bound sagas for reliable task execution.

Simple LLM loop architectures are insufficient for building robust autonomous agents

Late-bound sagas provide a structured approach to handle complex, multi-step agent workflows

This pattern enables better error handling, task orchestration, and execution reliability

The distinction matters for production AI systems that need to perform consistently across varied scenarios