Building production-grade AI agents requires more than just prompt engineering. This article explores key engineering challenges in agent loops: how context enters tasks, how tasks self-validate, how long-running executions correct themselves, and how these experiences are reused by teams and future agents. The author provides actionable tips for each stage, emphasizing the importance of structured context injection and iterative validation. These insights are critical for teams scaling from prototype to production agent systems. The advice is framework-agnostic and applicable to various agent architectures.
Practical tips for building reliable AI agent loops, focusing on context management, self-validation, and team reuse.