Seven Common Ways AI Agents Fail in Production and How to Fix Them

AI agents deployed in production environments consistently exhibit a set of recurring failure patterns that often go undetected by standard observability tools. Common issues include tool-call loops where agents repeat identical actions without making progress, silent context degradation as the model's memory window fills with stale data, and cost overruns caused by task-to-model mismatches. These failures are difficult to catch because they rarely trigger explicit errors, instead manifesting as gradual quality decline or runaway token consumption. Engineers are advised to track information gain, context pressure, and cost acceleration as proactive signals, and to implement automated interventions such as context compression, circuit-breakers, and mid-session model escalation.