Building Production AI Agents Is a Software Engineering Problem, Not an AI One

A developer with hands-on experience building autonomous AI agents argues that real-world deployment exposes failure modes that simple demos never reveal. The core challenge lies not in the language model itself, but in the surrounding infrastructure — including tool integrations, error handling, and state management. Agents that call APIs, read databases, or send emails face constant risks like network timeouts, expired authentication, and rate limiting, requiring robust engineering patterns like retries and circuit breakers. Context window management is another critical issue, as long-running agents accumulate noise over time, degrading decision quality without deliberate summarization and memory pruning strategies. The author concludes that predictability, observability, and solid orchestration — not model creativity — are what separate reliable production agents from impressive but fragile demos.