Token consumption in agentic LLM workflows is unpredictable and can vary 10x+ per task—TokenCast forecasts it accurately by tracking execution segments and context growth, improving budget planning by 14.5% on average.
TokenCast predicts how many tokens an LLM agent will consume during task execution, which varies wildly across runs due to tool use and growing context. It learns cost patterns for each execution step and updates predictions as the agent runs, enabling better budget control without extra LLM calls.