Infrastructure Patterns for Production AI Agents
Infrastructure Patterns for Agentic Applications
Building production-ready AI agents requires moving beyond simple web requests to handle long-running, stateful, and non-deterministic loops. I explore three core patterns: using queues to decouple work from requests, implementing idempotency and compensation for safe retries, and leveraging workflow engines to orchestrate complex branching logic. These strategies ensure your agents remain resilient and scalable in real-world environments.
If your agent is tied to a single HTTP request, you've coupled your application's reliability to the wall-clock time of a non-deterministic loop.