{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A workflow needs specialist review, but extra agents may add more cost than useful work.",
    "change": "Test the proposed division of work against a single-agent implementation.",
    "baseline": "One agent with the same tools and an equal total token and time budget.",
    "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": "Accepted outputs, coordination failures, wall-clock time, and total cost across all agents.",
    "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": "2610.01195",
  "title": "Federated Agent Optimization",
  "summary": "FAO is a problem formulation and taxonomy, not a deployable method; it would replace ad hoc or absent cross-organization sharing of agent experience (policies, memories, tools, rewards, skills) with controlled, privacy-constrained exchange, but specifies no concrete algorithm to adopt. Its cost is organizational and engineering complexity: abstracting private experience, adding protection mechanisms, and managing aggregation infrastructure across trust boundaries, none of which is priced or benchmarked here. What can fail is the central premise itself: abstracted experience may leak private information, aggregated capabilities may not transfer across heterogeneous agents and tasks, and the multi-objective trade-off among utility, privacy, and communication is asserted rather than validated.",
  "rationale": "This is a position paper proposing a research agenda with no implemented method, baseline comparison, or measured result, so it offers nothing actionable for a constrained-infrastructure production decision today.",
  "technique": "Federated Agent Optimization (FAO) framework",
  "family": "orquestracao_multiagente",
  "recommendation": "observar",
  "published": "2026-10-01",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.01195",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 135,
  "citations": 0,
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      "dimension": "security",
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    {
      "dimension": "data_and_training",
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      "dimension": "reproducibility",
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  "risks": [],
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  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
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}