You ship an AI agent to production. It runs smoothly for a week. Then one morning your LLM bill is 40% higher than expected—and you have no idea why. By the time you dig into logs, the damage is done.
The problem isn't that your agent broke loudly. It's that cost spikes are silent. Your agent succeeded. It returned tokens. It burned budget. And without a baseline to compare against, you won't see the spike until the bill arrives.
Why Baselines Matter (and Why Most Setups Skip Them)
Every LLM agent has a natural token consumption pattern. For a given input, it tends to use roughly the same number of tokens each time—within a predictable range. That range is your baseline.
When an agent deviates sharply from its baseline, something changed:
