Article URL: https://developers.googleblog.com/building-scalable-ai-agents-with-modular-prompt-transpilation/ Comments URL: https://news.ycombinator.com/item?id=48936621 Points: 4…

When you’re first building an AI agent, a single, monolithic system prompt is usually fine. You have a few instructions, maybe a tool definition or two, and everything lives in one readable file. But as you start using them for production purposes, that format simply just breaks down. Teams start layering on safety policies, domain-specific rules, formatting requirements, and escalation behaviors. Suddenly, you have your entire agent’s control plane within a single instruction file which is exactly where the trouble starts. This is a classic software engineering scaling problem. When you push every concern into a single file, you lose the ability to reason about the system. Collaboration becomes a nightmare, testing gets finicky, and a small change meant to improve one workflow can quietly break another. We typically see three main failure modes when prompts grow beyond a certain size: Templates are a good start, but they aren't enough. Production systems require deterministic builds, static validation, and CI/CD integration. The solution here is to treat prompts like build artifacts as opposed to just static text. Instead of maintaining one monolithic prompt file, you can author modular skill files. This allows you to reduce the scope of each file and encapsulate a specific behavior, which allows teams to separate concerns and iterate on components individually. This gives you the best of both worlds. The templating layer lets you compose shared instructions, inject environment-specific values, and make use of macros. But for the build system, every include is a dependency, and every variable is a requirement. The result is a deterministic, fully rendered artifact that you can test, audit, and diff before it ever reaches the model. We can then use a transpiler to resolve the template imports to generate a file that is ready to be ingested by an agent.