Building Reliable Integrations with AI Assistants

Using AI assistants to trigger or coordinate integrations is powerful, but production systems demand reliability. That means clear boundaries: defined inputs and outputs, validation before writes, and retries with backoff when external systems fail.

We recommend treating the assistant as an orchestrator that calls well-tested integration services, rather than having the model generate arbitrary API calls. Combine that with human-in-the-loop for high-impact or irreversible actions, and you get both flexibility and control.

This article covers patterns we use to keep AI-driven integrations predictable and maintainable.