{
  "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": "2609.19759",
  "title": "Rethinking Multi-Agent Collaboration: When More Is Less",
  "summary": "SAIGE replaces fixed multi-agent pipelines and monolithic single-agent harnesses with a dynamically evolving collaboration graph in which agents are spawned on demand and edges are semantic dependencies established via content-based retrieval, reducing context overhead. The cost is added orchestration complexity, retrieval infrastructure for dependency edges, and the engineering burden of incremental graph management. It can fail on tightly coupled sequential workflows, where the paper itself finds single-agent harnesses superior, and scaling the agent pool or recursion depth does not reliably improve outcomes.",
  "rationale": "The paper's core finding is conditional benefit on sparse-dependency long-horizon tasks, so adoption is justified only after validating that the reader's workload matches that structure.",
  "technique": "Semantic-aware incremental multi-agent graph (SAIGE)",
  "family": "orquestracao_multiagente",
  "recommendation": "testar",
  "published": "2026-09-17",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.19759",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 2,
  "citations": 0,
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      "dimension": "compatibility",
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    },
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      "dimension": "security",
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    },
    {
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  "risks": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations": [],
  "equations_status": "not_fetched",
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}