Agent cost is unpredictable — you cannot budget, price, or cap spending

Running an AI agent on a real task costs anywhere from $0.10 to $50+ and there is no way to predict the cost before execution. The same task can cost 10x more on a second run if the agent takes a different reasoning path. So what? If you are a founder building a product powered by agents, you cannot set a price for your product because you do not know your own costs. If you are an enterprise buyer, your finance team will block agent adoption because they cannot forecast spend. If you are a developer, you live in fear of runaway loops that drain your API budget overnight. Why does this matter in the first place? Every other computing resource — cloud VMs, storage, bandwidth, even GPU time — has predictable per-unit pricing. You can estimate costs before committing. Agent costs are fundamentally unpredictable because the number of LLM calls depends on the model's runtime reasoning, which varies with task complexity, tool call results, and stochastic sampling. This breaks every standard financial planning model. The structural reason this persists: no agent framework provides pre-execution cost estimation, per-run budget caps, or cost-aware planning where the agent considers cheaper alternative approaches. The economic feedback loop is missing — the agent has no incentive to be efficient because it does not see the bill.