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  "name": "Agent Success Score: measuring AI visibility and usability | Hyperize",
  "title": "Agent Success Score: measuring AI visibility and usability | Hyperize",
  "description": "How Hyperize measures whether AI agents find an offer and complete a task: visibility, usability, evidence and a score for each agent role.",
  "hook": "The Agent Success Score measures how well a brand enables AI agents to complete a defined customer task. It combines AI Visibility, AI Usability and Evidence into a value from 0 to 10, separately for each agent role. The underlying tests show what worked, where agents stopped and under which conditions.",
  "summary": "The Agent Success Score measures how well a brand enables AI agents to complete a defined customer task. It combines AI Visibility, AI Usability and Evidence into a value from 0 to 10, separately for each agent role. The underlying tests show what worked, where agents stopped and under which conditions.",
  "url": "https://www.hyperize.ai/en/methodology/agent-success-score",
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  "alternateLanguage": {
    "de": "https://www.hyperize.ai/de/methodology/agent-success-score"
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  "datePublished": "2026-07-02",
  "dateModified": "2026-10-04",
  "nextReview": "2026-11-03",
  "confidence": "A",
  "evidenceTier": "proprietary",
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    "title": "Agent Success Score: measuring AI visibility and usability | Hyperize",
    "description": "How Hyperize measures whether AI agents find an offer and complete a task: visibility, usability, evidence and a score for each agent role.",
    "definition": "The Agent Success Score measures how well a brand enables AI agents to complete a defined customer task. It combines AI Visibility, AI Usability and Evidence into a value from 0 to 10, separately for each agent role. The underlying tests show what worked, where agents stopped and under which conditions.",
    "standout": "The score summarizes the test. The evidence explains the result.",
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    "disambiguationNote": "This is neither a general benchmark of AI models nor a success rate for a support team.",
    "whyOne": "A valid file or correct markup shows that a technical requirement is met. To understand the customer journey, we also test whether agents find the offer, read the relevant facts and reach the agreed endpoint [S1].",
    "whyTwo": "The task and test limits are fixed in advance. A technical barrier, a missing permission and a deliberate stop before submission are recorded separately. A failed run alone does not establish its cause or prove a lost sale [S2].",
    "gatesIntro": "Being recommended does not mean an agent can use an offer. A usable offer may still go undiscovered. We measure both questions separately, with Evidence as the supporting layer.",
    "formula": "Agent Success Score = (AI Visibility × 0.20) + (AI Usability × 0.70) + (Evidence × 0.10)",
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    "trust": "A query audit tests discovery. Agent runs test access and actions on the live offer. These methods examine different parts of the journey; agreement can strengthen a finding but does not remove their limits. Tasks are fixed before measurement, and published results must be read with their scope and date [S2].",
    "lanesIntro": "A shopping agent has a different job from a recruiting or press agent. The model distinguishes six roles, called lanes. A result for one role cannot stand in for the others [S4].",
    "coverage": "Each fully measured role has its own score. Missing measurements stay explicit. The DAX 40 comparison uses the shared Commerce test set; separate Talent tests are also published. Those application tests alone do not establish a complete Talent score [S4].",
    "scope": "The definition and weights are public. Detailed scoring logic and raw run records remain within the engagement. Results apply to the tested task, access route, systems and date. A 6.5 score is not a 65% probability of purchase. Seven self-test prompts provide observations, not this score. Rankings, citations and future task completion are not guaranteed.",
    "cta": "Discuss my customer task",
    "sourcesNote": "The internal scoring definition is not a public source. Linked pages show the public task-selection rules and historical examples. This page updates the explanation; it does not remeasure any brand.",
    "chip": "Methodology · Concept",
    "h1": "Agent Success Score.",
    "distLabel": "What it is not",
    "distTitle": "A brand metric, not a support metric.",
    "whyLabel": "Why this exists",
    "gatesLabel": "How it is built",
    "gatesTitle": "Two gates. Both must open.",
    "formulaLabel": "Composition · published",
    "trustLabel": "Why trust it",
    "trustTitle": "Two complementary checks, one finding.",
    "lanesLabel": "The lane model",
    "lanesTitle": "One score per lane. Never blended.",
    "scopeLabel": "On scope",
    "scopeTitle": "The composition, not the derivation.",
    "relatedLabel": "Related",
    "relatedTitle": "The cluster around this metric.",
    "faqLabel": "Questions",
    "faqTitle": "What people ask.",
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    "updated": "Last updated",
    "review": "Next review",
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      "question": "Can the agent proceed on your website?",
      "body": "We test which agent classes can read facts and perform permitted steps. The value is derived from the observed access profile and the endpoint reached. It does not replace the test description [S1].",
      "weight": 0.7
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      "question": "Can the result be traced to evidence?",
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        "agentJob": "Apply"
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        "agentJob": "Research"
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      "cta": "Read the rules",
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      "body": "Pages, data and functions agents can read and use. Turn a finding into the right implementation.",
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      "eyebrow": "Public examples",
      "title": "DAX 40 Agent Success Index.",
      "body": "The methodology applied to real brands: measured tasks, results and limits in context.",
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      "a": "The Agent Success Score measures how well a brand enables AI agents to complete a defined customer task. It combines AI Visibility, AI Usability and Evidence into a value from 0 to 10, separately for each agent role. The underlying tests show what worked, where agents stopped and under which conditions."
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      "q": "Is it a service bot's success rate?",
      "a": "We assess the offer an incoming AI agent uses: a website, its data and permitted functions. A contact-center agent success rate instead measures how often support agents resolve an issue."
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      "q": "How is the Agent Success Score calculated?",
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    {
      "id": "S1",
      "publisher": "Hyperize",
      "title": "Internal scoring definition",
      "date": "May 2026",
      "url": null,
      "type": "Internal methodology",
      "supports": "The two measurement axes, derived usability and public composition weights. Not an independently accessible source.",
      "path": null
    },
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      "id": "S2",
      "publisher": "Hyperize",
      "title": "Task Selection Doctrine",
      "date": "May 2026",
      "url": "https://www.hyperize.ai/en/methodology/task-selection",
      "type": "Methodology",
      "supports": "Rules for selecting fair tasks and separating direct routes from intermediaries.",
      "path": "https://www.hyperize.ai/en/methodology/task-selection"
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      "publisher": "Hyperize",
      "title": "DHL Group: published test",
      "date": "Q2 2026",
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      "id": "S4",
      "publisher": "Hyperize",
      "title": "DAX 40 Agent Success Index",
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      "url": "https://www.hyperize.ai/en/dax40-index",
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      "supports": "The shared Commerce test set and separately reported application tests. This explanation does not remeasure any brand.",
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    "The Agent Success Score measures how well a brand enables AI agents to complete a defined customer task. It combines AI Visibility, AI Usability and Evidence into a value from 0 to 10, separately for each agent role. The underlying tests show what worked, where agents stopped and under which conditions.",
    "The score summarizes the test. The evidence explains the result.",
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    "Each fully measured role has its own score. Missing measurements stay explicit. The DAX 40 comparison uses the shared Commerce test set; separate Talent tests are also published. Those application tests alone do not establish a complete Talent score [S4].",
    "The definition and weights are public. Detailed scoring logic and raw run records remain within the engagement. Results apply to the tested task, access route, systems and date. A 6.5 score is not a 65% probability of purchase. Seven self-test prompts provide observations, not this score. Rankings, citations and future task completion are not guaranteed."
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      "answer": "The Agent Success Score measures how well a brand enables AI agents to complete a defined customer task. It combines AI Visibility, AI Usability and Evidence into a value from 0 to 10, separately for each agent role. The underlying tests show what worked, where agents stopped and under which conditions."
    },
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      "question": "Is it a service bot's success rate?",
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    },
    {
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    },
    {
      "name": "Founding Program",
      "type": "audit-and-implementation",
      "cost": "EUR 4,500",
      "duration": "Agreed before kickoff; depends on scope and required access",
      "url": "https://www.hyperize.ai/en/founding-program",
      "description": "Agent Success Audit plus implementation of the agreed use case. We agree the pages, data, functions, access requirements and delivery schedule before work begins."
    }
  ],
  "hasPart": [
    {
      "@id": "https://www.hyperize.ai/en/methodology/agent-success-score#defined-term-agent-success-score",
      "name": "Agent Success Score",
      "type": "DefinedTerm"
    }
  ]
}