I built an open-source toolkit for finding the minimum token budget an AI agent needs to complete tasks successfully without reducing task quality.
· 8 min read
AI agents are getting more capable, but they are also getting increasingly expensive in a way that is easy to overlook.
A single agent run may repeatedly pay for a system prompt, conversation history, retrieved context, memory, tool schemas, tool outputs, intermediate reasoning, and the final response. When the workflow becomes multi-step, token consumption compounds quickly.
Most benchmarks ask a binary question: did the agent complete the task?
I wanted to ask a second one:
How many tokens did it actually need to succeed?
That question led me to build Suffice, an open-source toolkit for measuring the minimum token budget an AI agent needs to complete a task while preserving a required level of quality.