Your AI travel booking agent just charged a customer fourteen times for the same flight. It called the booking tool, got a vague response, was not sure if it worked, and tried again. And again. Fourteen calls where two would do, twenty-one seconds wasted, hundreds of thousands of burned tokens. This is the repeated tool call problem, and it is far more common than you think. The root cause is simple. Ambiguous feedback leaves agents uncertain whether an action succeeded, so they retry. Studies show an average of 3.2x overcalling when tools return unclear responses, and it compounds across multi-step workflows. Three complementary solutions address it at different layers. DebounceHook keeps a sliding window of recent calls and blocks duplicates before they execute. Clear SUCCESS and FAILED states redesign responses so the agent knows when to proceed or stop. LimitToolCounts caps the calls to any one tool as a safety net. You'll walk away with: • A DebounceHook implementation with sliding window detection • A structured tool response pattern with explicit completion states • A live demo cutting 14 calls to 2 and 21s to 4s

Outline: • The Token Waste Problem • DebounceHook: Detect and Block Duplicates • Clear SUCCESS/FAILED States: Prevention by Design • LimitToolCounts: Hard Ceiling Enforcement • Production Patterns and Wrap-Up