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<title>AI Validation Framework</title>
<meta name="description" content="Build a practical validation plan for AI models, agents, data, tools, outputs and autonomous systems." />
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<div class="container">
  <section class="hero">
    <div class="eyebrow">Validation · Open AI Assurance</div>
    <h1>AI Validation Framework</h1>
    <p class="lead">Build a practical validation plan for models, agents, data, tools, outputs, and autonomous AI systems. Select your system type and risk profile to generate a structured validation checklist.</p>
    <div class="badges">
      <span class="badge">Models</span><span class="badge">Agents</span><span class="badge">Data</span>
      <span class="badge">Tool Use</span><span class="badge">Reliability</span><span class="badge">Observability</span>
      <span class="badge">Production AI</span>
    </div>
  </section>

  <div class="grid">
    <section class="card">
      <h2>Build your validation plan</h2>
      <p>Choose the system you are validating and the context in which it will operate.</p>

      <label for="system">System type</label>
      <select id="system">
        <option value="model">AI model</option>
        <option value="agent">AI agent</option>
        <option value="tool">Tool-using AI system</option>
        <option value="rag">RAG / retrieval system</option>
        <option value="multimodal">Multimodal / omnimodal system</option>
        <option value="world">World model / physical AI</option>
        <option value="data">AI data pipeline</option>
      </select>

      <label for="stage">Lifecycle stage</label>
      <select id="stage">
        <option value="development">Development</option>
        <option value="preprod">Pre-production</option>
        <option value="production">Production</option>
        <option value="change">Revalidation after a major change</option>
      </select>

      <label for="risk">Operational impact</label>
      <select id="risk">
        <option value="low">Low — advisory or easily reversible</option>
        <option value="medium" selected>Medium — business workflow impact</option>
        <option value="high">High — significant autonomous or real-world impact</option>
      </select>

      <button onclick="buildPlan()">Generate validation plan</button>
      <p class="small">This tool is a planning aid, not a certification or legal assessment.</p>
    </section>

    <section class="card" id="result">
      <div class="result-head">
        <div>
          <h2>Your validation plan</h2>
          <p id="summary">Choose your system and generate a plan.</p>
        </div>
        <div class="score"><span>Coverage</span><strong id="score">—</strong><span class="small">dimensions</span></div>
      </div>
      <div id="content" class="small">The framework will recommend validation dimensions, evidence, and revalidation triggers.</div>
    </section>
  </div>

  <section class="card" style="margin-top:24px">
    <h2>Core validation matrix</h2>
    <p>No single metric validates every AI system. Good validation combines evidence from multiple layers.</p>
    <div class="matrix">
      <table>
        <thead><tr><th>Layer</th><th>Core question</th><th>Typical evidence</th><th>Common failure</th></tr></thead>
        <tbody>
          <tr><td>Data</td><td>Is the data suitable and representative?</td><td>Schemas, provenance, drift, leakage checks</td><td>Clean-looking data that does not represent deployment reality</td></tr>
          <tr><td>Model</td><td>Does the model perform reliably for the intended task?</td><td>Benchmarks, robustness, calibration, regression tests</td><td>Strong benchmark score but poor use-case performance</td></tr>
          <tr><td>Agent</td><td>Does the agent complete tasks safely and correctly?</td><td>Task success, trajectories, recovery, permission checks</td><td>Correct result through unsafe or unauthorized actions</td></tr>
          <tr><td>Tool use</td><td>Are tools selected and invoked correctly?</td><td>Tool selection, schema adherence, execution traces</td><td>Valid-looking call with wrong tool or arguments</td></tr>
          <tr><td>Output</td><td>Is the output structurally and semantically valid?</td><td>Schema checks, citations, domain rules</td><td>Well-formed output that is factually wrong</td></tr>
          <tr><td>System</td><td>Does the complete AI stack work correctly together?</td><td>End-to-end tests, failure injection, integration evidence</td><td>Individually correct components failing at interfaces</td></tr>
          <tr><td>Production</td><td>Does acceptable behavior persist after deployment?</td><td>Observability, traces, drift, incidents, re-evaluation</td><td>Silent degradation after model or environment changes</td></tr>
        </tbody>
      </table>
    </div>
  </section>

  <div class="info">
    <strong>Working definition:</strong> AI validation is the process of establishing evidence that an AI component or system behaves as intended, within defined requirements, constraints, environments, and risk tolerances.
  </div>

  <div class="footer">
    Open technical resource by the <strong>Validation</strong> organization on Hugging Face. Built for researchers, engineers, and enterprise AI teams.<br>
    Research & industry collaborations: <a href="mailto:agenten@magenta.de">agenten@magenta.de</a>
  </div>
</div>

<script>
const common = {
  data: ["Schema and integrity checks","Representative validation data","Leakage and contamination review"],
  model: ["Use-case benchmark suite","Robustness and edge-case testing","Regression checks across versions"],
  output: ["Structural output checks","Semantic correctness checks","Policy and business-rule checks"],
  system: ["End-to-end integration tests","Dependency and interface failure tests","Versioned validation evidence"],
  production: ["Tracing and observability","Drift and incident monitoring","Defined revalidation triggers"]
};

const specific = {
  model: [
    ["Model performance","Measure task performance on representative and difficult cases."],
    ["Calibration & confidence","Check whether confidence aligns with actual correctness where relevant."],
    ["Robustness","Test prompt variation, noise, edge cases, and degraded inputs."],
    ["Operational constraints","Measure latency, throughput, memory, context limits, and cost."]
  ],
  agent: [
    ["Task completion","Measure whether the agent actually completes the requested task."],
    ["Tool selection","Check whether the correct tool is selected at the correct time."],
    ["Trajectory quality","Inspect steps, retries, and unnecessary actions—not only the final answer."],
    ["Recovery behavior","Test failed calls, missing information, invalid results, and partial outages."],
    ["Permission boundaries","Verify that actions remain within authorization and approval rules."],
    ["Escalation behavior","Confirm that the agent stops or asks for help when required."]
  ],
  tool: [
    ["Tool selection accuracy","Validate whether the system chooses the appropriate external capability."],
    ["Argument validity","Check schemas, required fields, types, and parameter constraints."],
    ["Execution safety","Gate destructive or high-impact actions and verify authorization."],
    ["Tool-output validation","Ensure tool responses are checked before downstream use."],
    ["Error recovery","Test timeouts, invalid responses, unavailable tools, and partial failures."]
  ],
  rag: [
    ["Retrieval quality","Measure recall, relevance, ranking, freshness, and source coverage."],
    ["Grounding","Check whether generated claims are supported by retrieved evidence."],
    ["Citation quality","Validate attribution, source mapping, and unsupported claims."],
    ["Corpus drift","Monitor changes in indexed content and retrieval behavior."],
    ["Failure handling","Test missing evidence, contradictory sources, and low-recall queries."]
  ],
  multimodal: [
    ["Cross-modal consistency","Check whether text, image, audio, video, or sensor evidence agree."],
    ["Modality degradation","Test missing, noisy, delayed, or contradictory modalities."],
    ["Grounding","Verify that generated conclusions remain tied to the actual inputs."],
    ["Temporal consistency","Test reasoning across time for audio and video inputs."],
    ["Any-to-any behavior","Validate switching between multiple input and output modalities."]
  ],
  world: [
    ["State estimation","Check whether the system represents the current environment correctly."],
    ["Predictive consistency","Validate future-state prediction under realistic dynamics."],
    ["Action feasibility","Ensure planned actions are possible and compatible with constraints."],
    ["Simulation-to-reality transfer","Compare simulated performance with real-world behavior."],
    ["Safety envelopes","Test limits, fail-safe behavior, and human override."],
    ["Sensor degradation","Evaluate robustness to noisy, missing, or delayed sensor inputs."]
  ],
  data: [
    ["Schema validity","Validate structure, types, ranges, required fields, and constraints."],
    ["Provenance","Document sources, licenses, lineage, and generation processes."],
    ["Distribution coverage","Check whether the data represents the intended operating population."],
    ["Leakage & duplication","Detect train/test contamination and near duplicates."],
    ["Drift readiness","Define how changes in production data will be detected."]
  ]
};

const stageAdds = {
  development: [["Reproducibility","Version data, model, prompts, tools, and evaluation configuration."]],
  preprod: [["Release gate","Define explicit pass/fail criteria before deployment."],["Failure injection","Test dependency failures, malformed responses, and degraded conditions."]],
  production: [["Continuous validation","Use production traces, incidents, drift, and user corrections as evidence."],["Revalidation triggers","Define when model, tool, prompt, data, or policy changes require new validation."]],
  change: [["Change impact analysis","Identify which assumptions and evidence became invalid after the change."],["Regression suite","Re-run the most decision-relevant tests across old and new versions."]]
};

function buildPlan(){
  const system = document.getElementById("system").value;
  const stage = document.getElementById("stage").value;
  const risk = document.getElementById("risk").value;
  let checks = [...specific[system], ...stageAdds[stage]];

  if(risk === "medium"){
    checks.push(["Operational acceptance criteria","Define measurable thresholds for reliability, latency, errors, and escalation."]);
  }
  if(risk === "high"){
    checks.push(
      ["Independent review","Add a second validation path or human review for critical requirements."],
      ["Adversarial & stress testing","Test misuse, unusual conditions, boundary cases, and failure cascades."],
      ["Human intervention","Verify safe stop, approval, escalation, and override mechanisms."]
    );
  }

  const labels = {
    model:"AI model", agent:"AI agent", tool:"tool-using AI system", rag:"RAG / retrieval system",
    multimodal:"multimodal / omnimodal system", world:"world model / physical AI system", data:"AI data pipeline"
  };
  const riskLabels = {low:"low operational impact",medium:"medium operational impact",high:"high operational impact"};

  document.getElementById("summary").textContent =
    `Recommended validation coverage for a ${labels[system]} with ${riskLabels[risk]}.`;
  document.getElementById("score").textContent = checks.length;

  const html = `
    <div class="section"><h3>Recommended dimensions</h3>
      <div class="checks">${checks.map(c=>`<div class="check"><b>${c[0]}</b><span>${c[1]}</span></div>`).join("")}</div>
    </div>
    <div class="section"><h3>Evidence to preserve</h3>
      <div class="checks">
        <div class="check"><b>Version context</b><span>Model, dataset, prompt, tool, dependency, and environment versions.</span></div>
        <div class="check"><b>Test evidence</b><span>Inputs, outputs, metrics, failures, thresholds, timestamps, and methodology.</span></div>
        <div class="check"><b>Decision record</b><span>What passed, what failed, known limitations, and deployment restrictions.</span></div>
      </div>
    </div>
    <div class="section"><h3>Revalidate when</h3>
      <div class="checks">
        <div class="check"><b>System changes</b><span>Model, prompt, tool, routing, data source, permission, or infrastructure changes materially.</span></div>
        <div class="check"><b>Operating conditions change</b><span>New users, domains, geographies, workflows, or risk profiles are introduced.</span></div>
        <div class="check"><b>Production evidence changes</b><span>Drift, incidents, increased corrections, new failure patterns, or degraded reliability appear.</span></div>
      </div>
    </div>`;
  document.getElementById("content").innerHTML = html;
}

buildPlan();
</script>
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