Stateless LLM agents cause ~20% double-refunds in payment flows — here's a structural fix (benchmark) Hey r/learnmachinelearning I've been working on llm-nano-vm — not just another agent helper, but an execution model for LLM pipelines. Just released v0.5.0. The benchmarks tell a story I think is worth sharing. \--- The problem: stateless agents and double-execution LLM agents are stateless between tool calls. The model decides "retry this API call" — but nothing in the execution layer remembers what already succeeded. In payment flows, email sends, or any operation with side effects, this is a production failure mode, not a theoretical one. Minimal example. Refund pipeline: check eligibility → call payment API → retry on failure. LLM decides whether to retry based on the API response. res = api.refund() retry, tokens, \_ = llm\_decide(res) while retry and retries < MAX\_RETRIES: res = api.refund() # <-- no guard retry, tokens, \_ = llm\_decide(res) retries += 1 Nothing stops a second successful refund.…