The Token Trap
Optimizing purely for fewer tokens can degrade agent accuracy by forcing redundant calls
“In some cases, using fewer tokens might actually produce worse results because your agent might be trying to get the same accuracy by calling multiple times.”
A Microsoft Build session warns that naive token reduction strategies can backfire in agentic systems, where cutting context forces agents to make multiple calls to recover accuracy, potentially increasing total cost. The real optimization target should be task accuracy per dollar, not raw token count. This challenges a common cost-cutting instinct among teams deploying AI agents.