Senior Software Engineer @ Arogga | Leading backend architecture for e-commerce and health-tech | Writing about scalable systems, and real production lessons
Why AI Agents Fail in Production: The Engineering Problems Beyond the LLM
Building reliable AI agents requires more than better models. It requires orchestration, durable state, idempotency, observability, security boundaries, and deterministic failure recovery.
The fragile bridge between probabilistic intelligence and production reality. Image generated by the author using AI.
Introduction: The Demo Works. Production Is a Different Story.
An AI agent receives a customer’s refund request.
It calls the payment API. The payment provider successfully processes the refund, but the network connection times out before the response reaches the agent.
The agent assumes the operation failed and retries.
The customer receives two refunds.
The problem wasn’t the LLM’s reasoning ability. It was a missing idempotency mechanism.
This is the uncomfortable reality of moving AI agents from impressive demonstrations into production systems.
AI agents are becoming increasingly capable. They can read documents, call APIs, search databases, execute code, interact with external services, and complete multi-step tasks with minimal human intervention.
Senior Software Engineer @ Arogga | Leading backend architecture for e-commerce and health-tech | Writing about scalable systems, and real production lessons
Abul, your opening example illustrates an important engineering distinction: an agent may make a reasonable decision, yet the surrounding system can still execute that decision more than once. The double-refund failure is not primarily a problem of…
