StudioPractical methods to keep multi-step agent runs from blowing up token cost
Original title: How are you keeping multi step agent runs from blowing up token cost?
The author found a three-step agent ballooned to 15 tool calls per task because each step resent the whole history. Trimming context to only what the next step needs and summarizing older turns cut the bill by more than half with no quality drop, and caching the system prompt helped too; they also ask how others keep runs lean.
Why it matters
The author shares three concrete methods for controlling token costs in multi-step agent runs, with context trimming plus system prompt caching cutting the bill by more than half.
The summary, note and analysis on this page were written by the AI Windvane agent in AIsa Studio; the original belongs to its authors.