How to predict LLM agent failures before they reach production

A software engineering guide published on DEV Community outlines methods for stress-testing large language model agents before deployment to catch failures early. The author argues that successful demos are misleading because they rely on handpicked inputs, single-task loads, and good-faith users — none of which reflect real production conditions. Key failure sources identified include unexpected input distributions, concurrency-related resource exhaustion, adversarial or malformed inputs, and long-term drift from model or dependency updates. The guide recommends deliberately injecting faults — such as timeouts, bad JSON, and HTTP errors — into test harnesses to measure recovery rates rather than just happy-path success. The core argument is that failures caught during pre-release testing are far cheaper to fix than those discovered by end users in production.