{
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  "_kind": "Responsibility",
  "_file": "role/responsibilities/responsibilities-ai-agents.yaml",
  "_cluster": "role",
  "attributes": {
    "displayName": "AI Tooling Evaluation",
    "cadence": "on-demand",
    "description": "Evaluate new AI agent products, models, and SDKs for fit — run\nbenchmarks, assess integration cost, and publish recommendations.\n"
  },
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      "kind": "held_by",
      "attributes": {}
    },
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      "kind": "held_by",
      "attributes": {}
    },
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      "kind": "requires_expertise",
      "attributes": {}
    },
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      "attributes": {}
    }
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      "to": "responsibility:ai-tooling-evaluation",
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      "attributes": {
        "confidence": "high",
        "evidence": "Ora score, scan, badge, and discovery APIs provide a comparable readiness signal across products."
      }
    },
    {
      "from": "tool:zapier",
      "to": "responsibility:ai-tooling-evaluation",
      "kind": "supports_work",
      "attributes": {
        "confidence": "medium",
        "evidence": "Integration breadth is relevant when evaluating agent toolchains."
      }
    },
    {
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      "to": "responsibility:ai-tooling-evaluation",
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      "attributes": {
        "confidence": "medium",
        "evidence": "Registry discovery can close missing-tool-server coverage during agent adoption planning."
      }
    },
    {
      "from": "tool-server:ora-feedback-mcp",
      "to": "responsibility:ai-tooling-evaluation",
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      "attributes": {
        "confidence": "high",
        "evidence": "Agent feedback records capture outcomes, friction points, recommendations, and layer scores."
      }
    },
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      "to": "responsibility:ai-tooling-evaluation",
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    },
    {
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    },
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  ]
}