Sunghyeun Woo

After a failed tool call, whether an agent retries, fixes the arguments or stops to report depends not only on the error text but on how that feedback enters the conversation. Holding the error content fixed and varying only delivery across 13 open models from 3B to 70B parameters, keeping the failed exchange and appending a runtime note with guidance in a new user turn avoided a severe stopping failure in Llama models; implemented as an opt-in recovery layer inside an inference engine, this form raised ToolMaze success on explicit errors by 14.2 points with 7–8B models and 10.5 points with 32–70B models, with no measurable gain on Loud/Quiet.

After a failed tool call, whether an agent retries, fixes the arguments or stops to report depends not only on the error text but on how that feedback enters the conversation. Holding the error content fixed and varying only delivery across 13 open models from 3B to 70B parameters, keeping the failed exchange and appending a runtime note with guidance in a new user turn avoided a severe stopping failure in Llama models; implemented as an opt-in recovery layer inside an inference engine, this form raised ToolMaze success on explicit errors by 14.2 points with 7–8B models and 10.5 points with 32–70B models, with no measurable gain on Loud/Quiet.