{
  "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": "2610.20959",
  "title": "Same Context, Different Agents: A Behavioral Study of Semantic Layers, Orchestration and Compaction in Data Agents",
  "summary": "The study replaces blanket application of context-engineering techniques (semantic layers, orchestration, compaction) with selection conditioned on observed agent behavior, task demands, and accuracy-cost trade-offs. Adopting it costs diagnostic instrumentation to classify agents as investigators, premature answerers, or early stoppers, plus per-configuration evaluation across models and benchmarks rather than a single fixed pipeline. It can fail when a mismatched CET is applied: semantic layers can stop investigators from inspecting data, orchestration can induce overconfidence (a measured 5-point accuracy loss), and compaction can cause repeated steps.",
  "rationale": "The guidance is directly actionable on a single GPU or via APIs since it requires no training, only profiling agent behavior before applying semantic layers, orchestration, or compaction, but the accuracy effects are model- and task-dependent and therefore demand workload-specific validation before production adoption.",
  "technique": "Behavior-matched context engineering for data agents",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-10-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.20959",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 132,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The study replaces blanket application of context-engineering techniques (semantic layers, orchestration, compaction) with selection conditioned on observed agent behavior, task demands, and accuracy-cost trade-offs. Adopting it costs diagnostic instrumentation to classify agents as investigators, premature answerers, or early stoppers, plus per-configuration evaluation across models and benchmarks rather than a single fixed pipeline. It can fail when a mismatched CET is applied: semantic layers can stop investigators from inspecting data, orchestration can induce overconfidence (a measured 5-point accuracy loss), and compaction can cause repeated steps.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.20959",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "CETs improve accuracy only for the behavior group they match; for already-investigating agents, orchestration causes overconfidence costing 5 percentage points of accuracy, while combining CETs yields smaller gains and losses than their sum.",
      "basis": "inferred",
      "result": "",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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    },
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
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      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
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}