AI agents in production: what actually breaks

In a local development setup, AI agents feel like magic. You feed them a prompt, watch them execute tools, and get a beautiful result. But when you move those agents into a production queue, reality sets in.

In production, AI systems encounter high-concurrency API rate limits, unpredictable token costs, network timeouts, and context drift. If your agent depends on a linear loop of LLM calls, a single transient network hiccup will break the entire workflow.

To build durable AI automation, you must treat LLM calls as untrusted, asynchronous operations. Implementing robust retry states, explicit context window constraints, and schema validation on tool arguments is essential.

By using framework integrations like Mastra and OpenAI structured outputs, we can enforce strict JSON schemas on every agent action. We also store intermediate state in PostgreSQL at every step, allowing agents to resume execution exactly where they left off if a timeout occurs. Precision-engineered state management is the difference between a demo and a product.