{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A support or data assistant repeats work across related customer requests.",
    "change": "Try the paper's memory change in a replay of past tasks, with separate memory per customer.",
    "baseline": "Your current memory pipeline plus a no-memory control using the same agent.",
    "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": "Task success, stale memories, retrieval misses, response latency, and all memory maintenance costs.",
    "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": "2609.26760",
  "title": "Grow the Harness, Not the Context: From Strategy-Free Scaffolds to Reusable Specialist Agents",
  "summary": "The method replaces per-task in-context reconstruction of control logic with a shared executable harness that is iteratively grown from failure traces, reserving LLM calls for task-specific reasoning. It costs an offline optimization loop with function-level tracing, joint failure repair, and gated rollback, plus ongoing maintenance of an accumulating code artifact whose complexity grows with use. After adoption it can fail when repairs overfit to the training task distribution, when the success gate misses regressions not covered by held-out tasks, or when task drift makes the frozen control code obsolete.",
  "rationale": "The cost and small-model robustness claims align directly with constrained infrastructure, but the approach depends on a trace-repair-gate pipeline and benchmark-specific validation that the reader must replicate on their own workload before production adoption.",
  "technique": "Failure-guided growth of reusable agent harness code",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-09-22",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.26760",
  "independent_implementations": 4,
  "total_implementations": 4,
  "stars": 10,
  "citations": 0,
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  "report_available": false,
  "validation_tier": "unknown",
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  "equations_status": "not_fetched",
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