{ "schema_version": 1, "builder_plan": { "scenario": "Tool errors or unavailable services cause your agent to abandon recoverable tasks.", "change": "Inject repeatable tool failures and change only the recovery policy.", "baseline": "Your current retry and fallback behavior under the same injected failures.", "steps": [ "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.", "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.", "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.", "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test." ], "measure": "Recovered tasks, duplicate side effects, retries, time to recovery, and total cost.", "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation." }, "builder_review": null, "arxiv_id": "2609.29095", "title": "Where Does Exactly-Once Live? Model, Harness, and Tool-Contract Effects on Duplicate Side Effects in LLM Agents", "summary": "The technique replaces blind retry or model-level caution when a tool write times out or errors: a small harness guard that attaches an idempotency key to every write, letting the service deduplicate redeliveries. 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