TokenCast forecasts token consumption during agent runs with composable segment costs, cutting MAE by 14.5% on average across 96 comparisons
TokenCast learns a composable cost triple for each execution segment of an LLM agent (call count, net input-length change, and a cost residual), uses an exact composition identity to fold the cost of earlier context being re-read by every later call into a cumulative estimate, and refreshes the forecast from newly observed execution evidence without additional LLM calls; on SWE-bench Verified the mean cumulative prediction time is 32.8 ms per run, mean absolute error reduction against the strongest comparator averages 14.5% over 96 evaluated combinations across 4 task suites and 6 agent models, and offline budget-control replay uses 21.3% fewer tokens on average than a fixed-budget policy at matched trace completion.