When building AI agents, where do your prompts live? If they're hidden inside frameworks or scattered across configuration files, you're missing a fundamental principle of maintainable AI systems: treating prompts as first-class code citizens.
Unlock reliable, testable AI agents by treating your LLM as a parser, not an executor. Learn how converting natural language into structured tool calls leads to predictable, scalable systems.
After building production AI systems over the past few years, thanks to HumanLayer, I’ve learned that most agent failures aren’t about the LLM, they’re about architecture.
That’s why I’m creating a series of posts sharing the 12-Factor Agents methodology using Mastra.
In each part, I’ll break down one principle that transforms fragile prototypes into robust, production-ready AI agents.