{ "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": "2610.02001", "title": "Mingbird: A Local-First Agent Harness Enabling Small Open Models to Complete Real Tasks", "summary": "Replaces cloud-oriented agent harnesses (e.g., goose, opencode) with a Windows/Ollama local-first harness whose ten mechanisms (net-zero prefill budget, finish gate re-reading the task, signature-level loop detection) target small-model failure forms for 2-9B models on a single 24 GB-class machine. Costs are integration effort with a Windows/Ollama-specific stack, additional guard logic per task step (latency and maintenance complexity), and reliance on a young third-party tool rather than an established framework. 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