Die vorliegende Übersetzung wurde maschinell erstellt. Im Falle eines Konflikts oder eines Widerspruchs zwischen dieser übersetzten Fassung und der englischen Fassung (einschließlich infolge von Verzögerungen bei der Übersetzung) ist die englische Fassung maßgeblich.

Verwenden Sie Kiro CLI mit einer Automated Reasoning-Richtlinie

Sie können Kiro CLI verwenden, um Fragen zu Ihren Automated Reasoning-Richtlinien zu stellen, das Verhalten der verschiedenen Regeln zu verstehen und Änderungen zu beantragen, um fehlgeschlagene Tests oder Unklarheiten in der Richtlinie selbst zu beheben.

Voraussetzungen

Um Kiro CLI mit Ihren Automated Reasoning-Richtlinien zu verwenden, müssen Sie zunächst die folgenden Schritte ausführen:

Erste Schritte

Sie benötigen den ARN der von Ihnen erstellten Automated Reasoning-Richtlinie, um den Workflow mit Kiro CLI zu starten.

An dieser Stelle sollte Kiro CLI Ihnen eine kurze Beschreibung der Regeln und Variablen der Richtlinie geben. Kiro CLI sollte auch den Bericht zur Richtlinienqualität laden und Probleme wie ungenutzte Typen und Variablen zusammenfassen.

Lösung politischer Probleme

Sie können Kiro CLI verwenden, um politische Probleme zu lösen, die im Richtlinienbericht gemeldet wurden. Bitten Sie Kiro zunächst, Ihnen eine Zusammenfassung des Qualitätsberichts zu geben:

Can you give me a summary of the quality report for this policy?

Der Qualitätsbericht enthält eine Liste der ungenutzten Variablen, widersprüchlichen Regeln und unzusammenhängenden Regeln sowie anderer potenzieller Probleme mit der Richtlinie.

Widersprüchliche Regeln führen dazu, dass Ihre Richtlinie auf alle Überprüfungsanfragen IMPOSSIBLE reagiert. Sie können Kiro CLI bitten, den Konflikt zu erklären und eine Lösung vorzuschlagen:

Can you look at the conflicting rules, explain how they are used in the policy, why they conflict, and suggest a change such as deleting one of the rules or merging the logic from the two into a single rule?

Unbenutzte Variablen können dazu führen, dass das Überprüfungsergebnis TRANSLATION_AMBIGUOUS Ergebnisse zurückgibt. Sie können Kiro CLI bitten, Ihnen bei diesem Problem zu helfen:

I see the quality report lists some unused variables, can you get rid of them?

In ähnlicher Weise können mehrdeutige Variablen, die sich semantisch ähneln, dazu führen, dass das Validierungsergebnis Ergebnisse zurückgibtTRANSLATION_AMBIGUOUS. Sie können Kiro CLI bitten, Ihnen bei diesem Problem zu helfen:

Automated Reasoning checks translate input natural language into logical statements that use the schema of variables from the policy. Variables that are semantically similar - ambiguous - can cause issues with inconsistent translations. Can you take a look at the schema of variables and help me identify variables that have potentially overlapping meanings? If you find any, suggest changes like removing one of them or merging them. Variable changes are also likely to require corresponding rule changes.

Interaktion mit einer Richtlinie

Sie können Kiro CLI verwenden, um Ihre Richtlinie zu erkunden. Sie könnten Kiro CLI beispielsweise bitten, die Richtlinienregeln für einen bestimmten Bereich zusammenzufassen. Anhand der Beispielrichtlinie für Hausaufgaben könnten Sie Kiro CLI bitten, Sie über die Regeln zu informieren, die speziell Mathe-Hausaufgaben einschränken:

Can you tell me about the rules that constrain math homework? Explain the rules themselves and how they fit in the broader policy.

Sie können die Kiro CLI auch verwenden, um der Richtlinie Funktionen hinzuzufügen. Richtlinien für automatisiertes Denken umgehen häufig einige wichtige Ausgabevariablen, die die Absicht des Benutzers erfassen. Beispielsweise verwendet die Beispielrichtlinie zur Einreichung von Hausaufgaben eine isSubmissionAcceptable Variable als Ergebnis ihrer Regeln. Sie können Kiro CLI verwenden, um die Richtlinie so zu ändern, dass andere mögliche Benutzerfragen erfasst werden.

Bearbeitung oder Überarbeitung Ihrer Richtlinie

Sie können Kiro CLI fragen, ob die Richtlinie bestimmte Fragen Ihrer Benutzer validieren kann. Zum Beispiel konzentriert sich die Hausaufgabenrichtlinie darauf, festzustellen, ob die Einreichung von Hausaufgaben zulässig ist. Sie können eine positive Antwort von Kiro CLI mit dieser Frage erwarten:

Can this policy validate answers about whether homework submissions are acceptable?

Wenn Sie in der Musterrichtlinie für Hausaufgaben nach einer Verzögerung bei der Einreichung von Hausaufgaben fragen, erklärt Kiro CLI, dass die Richtlinie diese Fragen nicht beantworten kann:

Can this policy validate answers about permissible delays in homework submissions?

Kiro CLI erklärt, was die Richtlinie kann und was nicht:

> Yes, but with limitations. The policy can validate whether a late submission is acceptable, but it cannot answer general questions about permissible delays.

What it CAN validate:
- Whether a specific late submission is acceptable given the circumstances
- Example: "Student submitted 2 days late with a doctor's note because they were sick before the deadline" → The policy will determine if this is acceptable (it would be)

What it CANNOT validate:
- General questions like "How many days late can a student submit homework?"
- Questions about delay policies without specific submission details
- Hypothetical scenarios without concrete variable values

The late submission logic:
The policy allows late submissions (submissionDate > 0) ONLY if:
1. The student has a valid doctor's note (hasDoctorNote = true), AND
2. The student was sick before the original deadline (wasSickPriorToDeadline = true)

Without both conditions met, any submission after the due date (submissionDate > 0) is automatically rejected.

So it's designed to validate specific submission instances against the rules, not to answer policy interpretation questions.
    

Wenn Sie etwas entdecken, das die Richtlinie nicht kann, können Sie Kiro CLI bitten, die Richtlinie zu aktualisieren, um diese Informationen zu erfassen, indem Sie die folgende Aufforderung verwenden:

I would like the policy to also validate answers about by how many days homework submissions can be delayed. Homework submissions accompanied by a doctor note can be up to 7 days late. Suggest the necessary variables and rules to capture these new questions.

Als Antwort auf diese Aufforderung schlägt Kiro CLI eine Reihe von Variablen und Regeln vor, die der Richtlinie hinzugefügt werden können, um die neue Art von Fragen zu validieren. Überprüfe die Vorschläge und wenn sie mit deiner Absicht übereinstimmen, kannst du Kiro CLI anweisen, die Anmerkung von Automated Reasoning Checks zu verwenden, APIs um die folgenden Änderungen an der Richtlinie vorzunehmen:

Looks good. Can you use the annotation APIs to submit these changes to the policy.

Sobald Kiro CLI bestätigt hat, dass die Anmerkungen bereit sind, können Sie Ihre Richtlinie in der Konsole öffnen, um die Anmerkungen zu überprüfen. Wenn die Anmerkungen korrekt sind, wählen Sie Apply Annotations.

Nachdem Sie die Anmerkungen übernommen haben, bitten Sie Kiro CLI, die neueste Version der Richtlinie neu zu laden, um sicherzustellen, dass Kiro CLI mit einer aktuellen Kopie funktioniert:

I applied the annotations. Reload the latest build of the policy.

Behebung fehlgeschlagener Tests

Eine gute Methode, um zu testen, ob Ihre Richtlinie für automatisiertes Denken die von Ihrer Anwendung generierte natürliche Sprache validieren kann, ist die Verwendung von Tests. Nachdem Sie Fragen und Antworten zu Tests mit den erwarteten Ergebnissen erstellt haben, können Sie Kiro CLI verwenden, um zu verstehen, warum ein Test nicht das erwartete Ergebnis zurückgegeben hat, und die Richtlinie anpassen. Weitere Informationen zum Erstellen und Ausführen von Tests finden Sie unter. Testen einer Automated-Reasoning-Richtlinie

Nächste Schritte

Sobald Sie mit der Automated Reasoning-Richtlinie zufrieden sind, können Sie sie für die Verwendung in Amazon Bedrock Guardrails bereitstellen. Weitere Informationen finden Sie unter Implementieren der Automated-Reasoning-Richtlinie in die Anwendung.

API-Kontext-Aufforderung zur automatischen Argumentationsrichtlinie

Kopieren Sie den folgenden Inhalt und speichern Sie ihn in einer Markdown-Datei in Ihrem Projektordner für Kiro CLI. Diese Aufforderung bietet Kiro CLI den Kontext, den es benötigt, um mit der Automated Reasoning-Richtlinie APIs korrekt zu arbeiten.

# Automated Reasoning Policy APIs and Workflows

## Table of Contents

### Core APIs
- Policy Management
- Policy Versions
- Build Workflows
- Test Management
- Annotations & Scenarios

### Build Workflow Types
- INGEST_CONTENT Workflow
- REFINE_POLICY Workflow
- IMPORT_POLICY Workflow

### Annotation Type Reference
- Type Management Annotations
- Variable Management Annotations
- Rule Management Annotations
- Natural Language Rule Creation
- Feedback-Based Updates

### Common Workflows
1. Getting Started (New Policy)
2. Building Policy from Document
3. Policy Development Cycle
4. REFINE_POLICY Workflow (Annotation-Based)

### Testing Workflow
1. Primary Approach: Scenarios API (Recommended)
2. Secondary Approach: Test Cases (User Experience)
3. Test Result Analysis and Troubleshooting

### Build Workflow Monitoring
- Check Build Status
- List Build History
- Best Practice: Clean Build Management
- Troubleshooting Build Failures

### Build Workflow Assets
- Asset Types
- Understanding Conflicting Rules
- Understanding Disjoint Rule Sets
- Advanced Quality Report Analysis

### Additional Topics
- Policy Version Export
- Key Concepts
- Important Format Requirements
- Policy Modeling Best Practices
- ARN Formats

## Core APIs

### Policy Management
- `create-automated-reasoning-policy` - Create initial policy (returns policy ARN)
- `get-automated-reasoning-policy` - Retrieve policy (DRAFT version by default with unversioned ARN)
- `update-automated-reasoning-policy` - Update DRAFT policy with new definition
- `delete-automated-reasoning-policy` - Delete policy
- `list-automated-reasoning-policies` - List all policies

### Policy Versions
- `create-automated-reasoning-policy-version` - Snapshot DRAFT into numbered version
- `export-automated-reasoning-policy-version` - Export specific policy version

### Build Workflows
- `start-automated-reasoning-policy-build-workflow` - Start build process
- `get-automated-reasoning-policy-build-workflow` - Get build workflow status
- `cancel-automated-reasoning-policy-build-workflow` - Cancel running build
- `delete-automated-reasoning-policy-build-workflow` - Delete build workflow
- `list-automated-reasoning-policy-build-workflows` - List build workflows
- `get-automated-reasoning-policy-build-workflow-result-assets` - Get compiled policy assets

### Test Management
- `create-automated-reasoning-policy-test-case` - Create test case
- `get-automated-reasoning-policy-test-case` - Get test case details
- `update-automated-reasoning-policy-test-case` - Update test case
- `delete-automated-reasoning-policy-test-case` - Delete test case
- `list-automated-reasoning-policy-test-cases` - List test cases
- `start-automated-reasoning-policy-test-workflow` - Run tests
- `get-automated-reasoning-policy-test-result` - Get test results
- `list-automated-reasoning-policy-test-results` - List test results

### Annotations & Scenarios
- `get-automated-reasoning-policy-annotations` - Get policy annotations
- `get-automated-reasoning-policy-next-scenario` - Get next test scenario

**Important**: Do NOT use `get-automated-reasoning-policy-annotations` or 
`update-automated-reasoning-policy-annotations` for the `REFINE_POLICY` workflow. Annotations are passed directly in the `start-automated-reasoning-policy-build-workflow` call.

## Build Workflow Types

1. **INGEST_CONTENT** - Process documents to create/extract policy rules
2. **REFINE_POLICY** - Refine and improve existing policies using annotations
3. **IMPORT_POLICY** - Import policies from external sources

### INGEST_CONTENT Workflow
- **Purpose**: Extract policy rules from documents (PDF/TXT)
- **Input**: Documents + optional existing policy definition
- **Use Cases**: Document-to-policy conversion, incremental policy building
- **Content Structure**: `workflowContent.documents[]`

**CRITICAL: Complete Policy Definition for Incremental Building**

When adding documents to an existing policy, you must include the complete current policy definition:

```json
// CORRECT - Incremental policy building
{
  "policyDefinition": {
    "version": "1.0",
    "types": [/* ALL existing types */],
    "rules": [/* ALL existing rules */],
    "variables": [/* ALL existing variables */]
  },
  "workflowContent": {
    "documents": [/* New documents to process */]
  }
}
```

### REFINE_POLICY Workflow
- **Purpose**: Iteratively improve policies with targeted modifications
- **Input**: Policy definition + annotations for specific changes
- **Use Cases**: Kiro CLI suggestions, test-driven improvements, feedback-based refinement
- **Content Structure**: `workflowContent.policyRepairAssets.annotations[]`

**CRITICAL: Complete Policy Definition Required**

ALL build workflows require the COMPLETE existing policy definition in the `policyDefinition` section, not just the changes you want to make.

**REFINE_POLICY Annotation Types:**

**Top-Level Annotations:**
- **Type Management**: `addType`, `updateType`, `deleteType`
- **Variable Management**: `addVariable`, `updateVariable`, `deleteVariable`
- **Rule Management**: `addRule`, `updateRule`, `deleteRule`
- **Natural Language Rules**: `addRuleFromNaturalLanguage`
- **Feedback-Based Updates**: `updateFromRulesFeedback`, `updateFromScenarioFeedback`

**Sub-Operations (only within `updateType`):**
- `addTypeValue`, `updateTypeValue`, `deleteTypeValue` - Used to modify values within an existing custom type

**important**: Only create rules in if/then format.

## Annotation Type Reference

### Type Management Annotations

#### `addType` - Create New Custom Type
```json
{
  "addType": {
    "name": "ApprovalStatus",
    "description": "Status values for approval requests",
    "values": [
      {
        "value": "PENDING",
        "description": "Request is awaiting approval"
      },
      {
        "value": "APPROVED",
        "description": "Request has been approved"
      },
      {
        "value": "REJECTED",
        "description": "Request has been rejected"
      }
    ]
  }
}
```

#### `updateType` - Modify Existing Custom Type
```json
{
  "updateType": {
    "name": "ApprovalStatus",
    "newName": "RequestStatus",
    "description": "Updated status values for all request types",
    "values": [
      {
        "addTypeValue": {
          "value": "ESCALATED",
          "description": "Request escalated to higher authority"
        }
      },
      {
        "updateTypeValue": {
          "value": "PENDING",
          "newValue": "WAITING",
          "description": "Request is waiting for review"
        }
      },
      {
        "deleteTypeValue": {
          "value": "REJECTED"
        }
      }
    ]
  }
}
```

#### `deleteType` - Remove Custom Type
```json
{
  "deleteType": {
    "name": "ObsoleteType"
  }
}
```

### Variable Management Annotations

#### `addVariable` - Create New Variable
```json
{
  "addVariable": {
    "name": "requestAmount",
    "type": "real",
    "description": "The monetary amount of the approval request in USD"
  }
}
```

#### `updateVariable` - Modify Existing Variable
```json
{
  "updateVariable": {
    "name": "requestAmount",
    "newName": "approvalAmount",
    "description": "The monetary amount requiring approval in USD (updated description)"
  }
}
```

#### `deleteVariable` - Remove Variable
```json
{
  "deleteVariable": {
    "name": "obsoleteVariable"
  }
}
```

### Rule Management Annotations

#### `addRule` - Create New Rule (SMT-LIB)
```json
{
  "addRule": {
    "expression": "(=> (and (= userRole MANAGER) (< requestAmount 10000)) (not approvalRequired))"
  }
}
```

#### `updateRule` - Modify Existing Rule
```json
{
  "updateRule": {
    "ruleId": "A1B2C3D4E5F6",
    "expression": "(=> (and (= userRole MANAGER) (< requestAmount 5000)) (not approvalRequired))"
  }
}
```

#### `deleteRule` - Remove Rule
```json
{
  "deleteRule": {
    "ruleId": "G7H8I9J0K1L2"
  }
}
```

### Natural Language Rule Creation

#### `addRuleFromNaturalLanguage` - Convert Natural Language to Rule
```json
{
  "addRuleFromNaturalLanguage": {
    "naturalLanguage": "Managers can approve expense requests up to $5,000 without additional authorization. Senior managers can approve up to $25,000."
  }
}
```

### Feedback-Based Updates

#### `updateFromRulesFeedback` - Improve Rules Based on Performance
```json
{
  "updateFromRulesFeedback": {
    "ruleIds": ["A1B2C3D4E5F6", "G7H8I9J0K1L2"],
    "feedback": "These rules are too restrictive for emergency scenarios. Add exception handling for urgent requests with proper escalation paths."
  }
}
```

#### `updateFromScenarioFeedback` - Improve Based on Test Scenarios
```json
{
  "updateFromScenarioFeedback": {
    "ruleIds": ["A1B2C3D4E5F6"],
    "scenarioExpression": "(and (= requestType EMERGENCY) (= userRole MANAGER) (> requestAmount 10000))",
    "feedback": "Emergency requests should have different approval thresholds. Current rule blocks legitimate emergency expenses."
  }
}
```

**Important**: Do NOT use `get-automated-reasoning-policy-annotations` or `update-automated-reasoning-policy-annotations` for the `REFINE_POLICY` workflow. Annotations are passed directly in the `start-automated-reasoning-policy-build-workflow` call.

## Common Workflows

### 1. Getting Started (New Policy)

**CRITICAL: Always Create Policy First**

You must create a policy before starting any build workflows.

```bash
# Step 1: Create initial policy (REQUIRED FIRST STEP)
aws bedrock create-automated-reasoning-policy \
  --region us-west-2 \
  --name "YourPolicyName"

# Step 2: Extract the policyArn from the response above, then start build workflow
aws bedrock start-automated-reasoning-policy-build-workflow \
  --region us-west-2 \
  --policy-arn "arn:aws:bedrock:us-west-2:123456789012:automated-reasoning-policy/abcd1234efgh" \
  --build-workflow-type INGEST_CONTENT \
  --source-content <policy-definition>

# Step 3: Get build results
aws bedrock get-automated-reasoning-policy-build-workflow-result-assets \
  --region us-west-2 \
  --policy-arn "arn:aws:bedrock:us-west-2:123456789012:automated-reasoning-policy/abcd1234efgh" \
  --build-workflow-id <workflow-id>
```

### 2. Building Policy from Document

**RECOMMENDED: Using CLI Input JSON File**

```bash
# Step 1: Encode PDF to base64 and create JSON file with base64 content
PDF_BASE64=$(base64 -i your-policy.pdf | tr -d '\n')

cat > ingest-policy.json << EOF
{
  "policyArn": "arn:aws:bedrock:us-west-2:123456789012:automated-reasoning-policy/your-actual-policy-id",
  "buildWorkflowType": "INGEST_CONTENT",
  "sourceContent": {
    "policyDefinition": {
      "version": "1.0",
      "types": [],
      "rules": [],
      "variables": []
    },
    "workflowContent": {
      "documents": [
        {
          "document": "$PDF_BASE64",
          "documentContentType": "pdf",
          "documentName": "Company Policy Document",
          "documentDescription": "Main policy document containing business rules and organizational guidelines."
        }
      ]
    }
  }
}
EOF

# Step 2: Use the JSON file
aws bedrock start-automated-reasoning-policy-build-workflow \
  --region us-west-2 \
  --cli-input-json file://ingest-policy.json
```

### 3. Policy Development Cycle

```bash
# 1. Import/process policy definition
aws bedrock start-automated-reasoning-policy-build-workflow \
  --build-workflow-type IMPORT_POLICY

# 2. Update DRAFT with processed definition
aws bedrock update-automated-reasoning-policy \
  --policy-arn <unversioned-arn> \
  --policy-definition <build-output>

# 3. Create versioned snapshot of DRAFT
aws bedrock create-automated-reasoning-policy-version \
  --policy-arn <unversioned-arn>
```

## Testing Workflow

### Primary Approach: Scenarios API (Recommended)

Use `get-automated-reasoning-policy-next-scenario` for comprehensive policy validation.

The Scenarios API is superior for testing because it:
- Tests formal logic directly - Validates policy rules work correctly
- AI-generated scenarios - Comprehensive coverage of edge cases and rule interactions
- Targets specific rules - Tests individual rules and combinations
- Always works - No natural language translation issues
- Intelligent test generation - AI understands policy logic deeply

```bash
# Generate intelligent test scenarios automatically
aws bedrock get-automated-reasoning-policy-next-scenario \
  --policy-arn "arn:aws:bedrock:region:account:automated-reasoning-policy/policy-id" \
  --build-workflow-id "workflow-123"
```

### Secondary Approach: Test Cases (User Experience)

Use manual test cases to validate natural language translation.

```bash
# Create test cases for natural language validation
aws bedrock create-automated-reasoning-policy-test-case \
  --policy-arn "arn:aws:bedrock:region:account:automated-reasoning-policy/policy-id" \
  --guard-content "It is 2:30 PM on a clear day" \
  --query-content "What color should the sky be?" \
  --expected-aggregated-findings-result "VALID"
```

### Test Result Analysis and Troubleshooting

**Understanding Test Results:**

**Scenarios API Results:**
- `expectedResult: SATISFIABLE` - Policy logic works correctly
- API errors or logic conflicts - Policy needs fixing with REFINE_POLICY

**Common Test Case Failure Modes:**

1. **TRANSLATION_AMBIGUOUS**
   - Problem: AI can't map natural language to policy variables
   - Solution: Improve variable descriptions with more natural language synonyms

2. **SATISFIABLE when expecting VALID**
   - Problem: Your expected result label is likely WRONG, not the policy
   - SATISFIABLE = "This scenario is logically consistent with the policy rules"
   - VALID = "This is the correct/expected answer according to the policy"
   - Solution: Change `expectedAggregatedFindingsResult` from `VALID` to `SATISFIABLE`

3. **Empty testFindings arrays**
   - Problem: Translation issues, not rule violations
   - Solution: Focus on improving natural language descriptions, not policy logic

## Build Workflow Monitoring

**Critical Build Limits**: The API supports maximum 2 total build workflows per policy, with only 1 allowed to be IN_PROGRESS at any time. When a build workflow completes, you can instruct the user to review the output using the console. 

### Check Build Status

```bash
aws bedrock get-automated-reasoning-policy-build-workflow \
  --policy-arn "arn:aws:bedrock:region:account:automated-reasoning-policy/policy-id" \
  --build-workflow-id "workflow-123"
```

### List Build History

```bash
aws bedrock list-automated-reasoning-policy-build-workflows \
  --policy-arn "arn:aws:bedrock:region:account:automated-reasoning-policy/policy-id" \
  --max-results 50
```

### Best Practice: Clean Build Management

```bash
# 1. Check existing builds before starting new ones
aws bedrock list-automated-reasoning-policy-build-workflows \
  --policy-arn <policy-arn> \
  --max-results 10

# 2. Delete old/completed builds if you have 2 already
aws bedrock delete-automated-reasoning-policy-build-workflow \
  --policy-arn <policy-arn> \
  --build-workflow-id "old-workflow-id" \
  --last-updated-at "2025-11-15T00:41:18.608000+00:00"

# 3. Now start your new build
aws bedrock start-automated-reasoning-policy-build-workflow \
  --policy-arn <policy-arn> \
  --build-workflow-type INGEST_CONTENT \
  --source-content <content>
```

## Build Workflow Assets

After a build workflow completes successfully, you can retrieve various assets. After you complete a build workflow, you can ask the user to check the build diff using the Automated Reasoning checks console.

### Asset Types

#### 1. POLICY_DEFINITION - The Main Output

```bash
aws bedrock get-automated-reasoning-policy-build-workflow-result-assets \
  --policy-arn "arn:aws:bedrock:region:account:automated-reasoning-policy/policy-id" \
  --build-workflow-id "workflow-123" \
  --asset-type "POLICY_DEFINITION"
```

**What it contains:**
- Compiled policy with extracted/refined rules, variables, and types
- SMT-LIB expressions for all rules
- Complete policy structure ready for deployment

#### 2. BUILD_LOG - Build Process Details

```bash
aws bedrock get-automated-reasoning-policy-build-workflow-result-assets \
  --policy-arn "arn:aws:bedrock:region:account:automated-reasoning-policy/policy-id" \
  --build-workflow-id "workflow-123" \
  --asset-type "BUILD_LOG"
```

**What it shows:**
- Document processing steps - What content was analyzed
- Extraction results - What rules, variables, and types were found
- Processing warnings - Content that couldn't be interpreted
- Success/failure status for each extraction step

#### 3. QUALITY_REPORT - Policy Quality Analysis

```bash
aws bedrock get-automated-reasoning-policy-build-workflow-result-assets \
  --policy-arn "arn:aws:bedrock:region:account:automated-reasoning-policy/policy-id" \
  --build-workflow-id "workflow-123" \
  --asset-type "QUALITY_REPORT"
```

**What it contains:**
- Conflicting rules - Rules that contradict each other
- Unused variables - Variables not referenced by any rules
- Unused type values - Enum values not used in rules
- Disjoint rule sets - Groups of rules that don't interact