{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An agent looks good in demos, but you cannot tell which product tasks it fails.",
    "change": "Use the study's evaluation idea to inspect a small set of tasks with human-checked answers.",
    "baseline": "Your current evaluator and a manually reviewed reference set.",
    "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": "Agreement with human review, missed failures, false alarms, and evaluation cost per task.",
    "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": "2610.10833",
  "title": "On the Clock: Towards Punctual and Productive Time-Budgeted AI Agents",
  "summary": "The approach replaces unstated or prompt-only time budgets with harness-exposed timing signals, optional deadline-enforcement hooks, and optionally GRPO training with budget-aware rewards. The harness mechanisms cost little (minor instrumentation and prompt tokens for timing feedback), while the RL variant adds training compute and reward-engineering complexity. It can fail to convert adherence into productivity: agents that respect the budget still fill extra time with repeated actions, and multi-budget GRPO training tends to collapse toward the shortest-budget strategy.",
  "rationale": "Timing injection in the harness is cheap and fits constrained infrastructure, but the paper shows adherence gains without corresponding task-performance gains, so adoption must be validated against the reader's actual workloads before committing to the RL variant.",
  "technique": "Harness-injected timing feedback for budgeted agents",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-10-07",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.10833",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 132,
  "citations": 0,
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    },
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      "dimension": "compatibility",
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      "source_page": null,
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    },
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      "dimension": "security",
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      "dimension": "data_and_training",
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    },
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      "dimension": "reproducibility",
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      "source_page": null,
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  "open_questions": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
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}