Silent Cost Spikes: Why Your AI Agent's Bill Can Jump 40% Without Any Errors

AI agents deployed in production can silently inflate LLM costs by 40% or more without triggering any errors or failed runs, making the problem hard to detect in real time. Each agent has a natural token consumption pattern, and sharp deviations from that baseline — caused by retry loops, accidental prompt growth, or tool recursion — signal something has gone wrong. Using statistical percentiles such as p50 and p95, rather than simple averages, provides a more reliable baseline because percentiles are resistant to outliers that skew averages. Effective anomaly detection requires both magnitude and persistence: a single high-token run may be legitimate, but multiple consecutive runs exceeding p95 with at least one above p99 indicates a systemic issue worth investigating. Setting up a baseline monitoring system that tracks these thresholds allows engineering teams to catch runaway cost patterns before the monthly bill arrives.