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      "text": "An AI agent is an AI system that can reason toward a goal, use tools or external context, and carry out multi-step work. The more autonomy it has, the more permissions, auditability, and human checkpoints matter.",
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      "text": "Evaluate AI agents by task success, safe execution, and human intervention load. Task completion rate | Successful tasks / attempted tasks | Measures practical usefulness Intervention rate | Human stops / executions | Shows whether autonomy is calibrated Safe execution rate | Policy-compliant executions / executions | Measures permission and audit health",
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        "Safe execution rate | Policy-compliant executions / executions | Measures permission and audit health"
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      "title": "含めるもの / 含めないもの",
      "text": "AI agents overlap with chatbots, workflow automation, and RPA, but differ by tool access and decision scope. Include | Planning, search, tool calls, file operations, API calls, validation, revision | Handles multi-step work Exclude | Unlimited autonomy, unapproved high-impact actions, ownerless decisions | Requires governance Make explicit | Tool set, data scope, approval gates, logs, stop conditions | Required for production use",
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        "Exclude | Unlimited autonomy, unapproved high-impact actions, ownerless decisions | Requires governance",
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        "text": "An AI agent is a system that interprets a goal, plans or selects steps, uses tools and data sources, and checks or updates its work across a task. Compared with a single chat response, an agent may combine search, file operations, API calls, planning, validation, and revision. Production agents need explicit boundaries for allowed tools, accessible data, human approvals, audit logs, and stop conditions. Without those boundaries, an agent can make mistakes faster and with broader operational impact.",
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        "text": "Teams can decide whether chat assistance is enough or whether tool-executing agents are justified. Classifying actions by approval requirement balances speed and safety. MCP or API integrations can be designed around tool descriptions, input schemas, permissions, and logs.",
        "items": [
          "Teams can decide whether chat assistance is enough or whether tool-executing agents are justified.",
          "Classifying actions by approval requirement balances speed and safety.",
          "MCP or API integrations can be designed around tool descriptions, input schemas, permissions, and logs."
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          "Start with low-risk read or draft workflows before allowing high-impact execution.",
          "Measure intervention, misoperation, permission, and user burden alongside success rate.",
          "Standard connectors such as MCP are useful, but tool exposure must be intentionally scoped."
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      },
      {
        "key": "drivers",
        "title": "何が数字を動かすか",
        "text": "Agent performance depends on tool design, permissions, evaluation, and failure boundaries as much as model capability. Tools | Narrow, well-described tools limit damage when something fails Permissions | Separate read, draft, execute, and external-send rights Evaluation | Long tasks need both intermediate and final checks Human review | Approval gates make autonomy safer to expand",
        "items": [
          "Tools | Narrow, well-described tools limit damage when something fails",
          "Permissions | Separate read, draft, execute, and external-send rights",
          "Evaluation | Long tasks need both intermediate and final checks",
          "Human review | Approval gates make autonomy safer to expand"
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      }
    ],
    "misunderstandings": [
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        "title": "判断するときの注意点",
        "text": "Agent usefulness and agent risk scale together. External sending, deletion, purchases, contracts, and permission changes should require human approval. Ambiguous tool descriptions can cause tools to be invoked for the wrong purpose. Without execution logs and replay information, incidents cannot be investigated well.",
        "items": [
          "External sending, deletion, purchases, contracts, and permission changes should require human approval.",
          "Ambiguous tool descriptions can cause tools to be invoked for the wrong purpose.",
          "Without execution logs and replay information, incidents cannot be investigated well."
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          "Agents do not need to be fully autonomous. Human checkpoints are often the safer design.",
          "A better model alone does not remove operational risk. Tool and permission design matter.",
          "Long tasks do not automatically improve efficiency. Without intermediate checks, rework can grow."
        ]
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        "title": "似ている言葉との違い",
        "text": "AI Agent | Carries out multi-step work toward a goal | Needs tools and evaluation Chatbot | Responds in conversation | Usually limited execution authority RPA | Automates fixed steps | Less flexible reasoning",
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        "text": "AI agents should be read together with MCP, tool use, evaluation, and prompt injection. Model Context Protocol | Standard for connecting tools and context | Often used in agent integrations Tool Use | Ability to call external systems | Defines execution scope Prompt Injection | Untrusted input can redirect actions | Especially important for agents",
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        "answer": "Not for high-impact actions. Use approval gates, logs, and stop conditions."
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        "question": "What should be automated first?",
        "answer": "Start with read, draft, summary, and research-support tasks that are easy to review and roll back."
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      "text": "Agent performance depends on tool design, permissions, evaluation, and failure boundaries as much as model capability. Tools | Narrow, well-described tools limit damage when something fails Permissions | Separate read, draft, execute, and external-send rights Evaluation | Long tasks need both intermediate and final checks Human review | Approval gates make autonomy safer to expand Tools | Narrow, well-described tools limit damage when something fails Permissions | Separate read, draft, execute, and external-send rights Evaluation | Long tasks need both intermediate and final checks Human review | Approval gates make autonomy safer to expand",
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      "text": "AI Agent | Carries out multi-step work toward a goal | Needs tools and evaluation Chatbot | Responds in conversation | Usually limited execution authority RPA | Automates fixed steps | Less flexible reasoning AI Agent | Carries out multi-step work toward a goal | Needs tools and evaluation Chatbot | Responds in conversation | Usually limited execution authority RPA | Automates fixed steps | Less flexible reasoning",
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      "text": "AI agents should be read together with MCP, tool use, evaluation, and prompt injection. Model Context Protocol | Standard for connecting tools and context | Often used in agent integrations Tool Use | Ability to call external systems | Defines execution scope Prompt Injection | Untrusted input can redirect actions | Especially important for agents Model Context Protocol | Standard for connecting tools and context | Often used in agent integrations Tool Use | Ability to call external systems | Defines execution scope Prompt Injection | Untrusted input can redirect actions | Especially important for agents",
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