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Capture with AI Agents ​

Overview ​

Capture is designed to support AI-assisted processes while maintaining human oversight. It provides the critical human-in-the-loop validation that ensures AI outputs are accurate, appropriate, and meet business requirements.

Why AI Needs Capture ​

The AI Challenge ​

AI systems can:

  • Process large volumes of data quickly
  • Identify patterns and make predictions
  • Automate repetitive tasks
  • Generate outputs based on training

But AI cannot:

  • Guarantee 100% accuracy
  • Understand business context fully
  • Make nuanced judgment calls
  • Be held accountable for decisions

The Solution ​

Capture provides:

  • Human validation checkpoint - Expert review of AI outputs
  • Deterministic outcomes - Clear human decision recorded
  • Accountability - Person responsible for approval identified
  • Quality assurance - Catch AI errors before they cause problems
  • Continuous improvement - Human feedback improves AI over time

Common AI + Capture Patterns ​

Pattern 1: AI Analysis with Human Approval ​

Workflow:

  1. AI analyzes engineering data
  2. AI generates report or recommendations
  3. Capture created automatically with AI output
  4. Human expert reviews AI analysis
  5. Expert approves or rejects with feedback
  6. If approved: Process continues automatically
  7. If rejected: AI retrained or manual process triggered

Example Use Cases:

  • AI-generated Bill of Materials review
  • Automated design compliance checking
  • Predictive maintenance recommendations
  • Automated classification suggestions

Pattern 2: AI-Assisted Human Review ​

Workflow:

  1. Human initiates review process
  2. AI pre-analyzes artifacts
  3. AI highlights potential issues
  4. Capture shows both artifacts and AI findings
  5. Human reviews with AI assistance
  6. Human makes final decision
  7. Decision and reasoning recorded

Example Use Cases:

  • Drawing quality checks with AI assistance
  • Document completeness validation
  • Specification compliance review
  • Technical document review

Pattern 3: Confidence-Based Routing ​

Workflow:

  1. AI processes tasks automatically
  2. AI calculates confidence score for each
  3. High confidence: Proceeds automatically
  4. Low confidence: Capture created for human review
  5. Human reviews uncertain cases
  6. Feedback improves AI confidence over time

Example Use Cases:

  • Automated document classification
  • Part numbering suggestions
  • Design rule checking
  • Data extraction validation

Designing AI + Capture Integrations ​

Template Configuration ​

Fields to include:

  • AI Confidence Score (numeric)
  • AI Model Version (text)
  • AI Recommendation (text or dropdown)
  • Human Decision (auto-populated on action)
  • Override Reason (required if disagreeing with AI)
  • Validation Notes (reviewer comments)

Example Template: "AI Output Validation":

  • Submission Type: Approve/Reject
  • Fields:
    • AI Confidence: 0.95
    • AI Recommendation: "Approve for Release"
    • Model: "Design-Check-v2.3"
    • Reviewer Notes: [Text area]
  • Security: Senior Engineers only
  • On Approve: Continue automated workflow
  • On Reject: Flag for manual processing and AI retraining

Workflow Integration ​

AI Workflow Step:

1. AI Task: Analyze Design
2. Decision Node: Confidence >= 0.9?
   - Yes: Proceed to Step 4
   - No: Create Capture
3. Capture: Human Review
   - Wait for human decision
   - If Approved: Proceed to Step 4
   - If Rejected: Go to Step 5 (Manual Process)
4. Automated Publishing
5. Manual Rework Process

AI Output Display in Capture ​

Presenting AI Results ​

Best practices:

  • Show AI confidence score prominently
  • Display AI reasoning (if available)
  • Highlight areas AI flagged
  • Provide context for AI recommendations
  • Link to AI analysis details

UI Example:

AI Analysis Results:
✓ Confidence: 92%
✓ Recommendation: Approve
✓ Model: Design-Check-v2.3

Issues Identified:
⚠️ Dimension tolerance wider than typical (Low severity)
✓ All required views present
✓ Material specifications complete

Reviewer Action Required:
Review AI findings and make final determination.

Human Feedback Loop ​

Capturing Feedback ​

When humans review AI outputs, capture:

  • Did human agree with AI recommendation?
  • If not, why not?
  • What did AI miss?
  • What did AI incorrectly flag?

Using Feedback ​

Short-term:

  • Complete current process appropriately
  • Document AI performance
  • Flag specific AI errors

Long-term:

  • Retrain AI models with human decisions
  • Improve AI confidence calibration
  • Refine AI algorithms
  • Adjust confidence thresholds

Confidence Score Management ​

Setting Thresholds ​

High Confidence (>90%):

  • Proceed automatically
  • Optional spot-check review

Medium Confidence (70-90%):

  • Always create Capture
  • Human review required
  • Standard priority

Low Confidence (<70%):

  • Always create Capture
  • Urgent/priority review
  • May need expert reviewer

Adjusting Over Time ​

As AI improves:

  • Raise automation threshold
  • Reduce review workload
  • Focus human effort on truly uncertain cases

As AI degrades (model drift):

  • Lower automation threshold
  • Increase review percentage
  • Investigate AI performance issues

Use Case Examples ​

Use Case 1: AI-Generated BOM Validation ​

Process:

  1. AI extracts BOM from CAD assembly
  2. AI confidence: 85% (medium)
  3. Capture created with BOM spreadsheet
  4. Engineer reviews:
    • Checks part quantities
    • Verifies part numbers
    • Validates material specs
  5. Engineer approves with minor edit note
  6. BOM published to ERP system
  7. Human feedback logged for AI improvement

Use Case 2: Drawing Compliance Check ​

Process:

  1. AI analyzes drawing against standards
  2. AI flags 3 potential issues
  3. AI confidence: 78% (medium-low)
  4. Capture created with drawing + AI findings
  5. Senior engineer reviews:
    • Issue 1: Valid (missing dimension)
    • Issue 2: False positive (AI misread note)
    • Issue 3: Valid (incorrect title block)
  6. Engineer rejects with corrections
  7. Feedback improves AI's note-reading capability

Use Case 3: Document Classification ​

Process:

  1. Batch of 100 documents submitted
  2. AI classifies each:
    • 85 documents: High confidence (>95%)
    • 12 documents: Medium confidence (80-95%)
    • 3 documents: Low confidence (<80%)
  3. 85 proceed automatically
  4. Capture created for 15 uncertain documents
  5. Classifier reviews and corrects
  6. All documents routed appropriately
  7. AI learns from corrections

Benefits of AI + Capture ​

For Organizations ​

  • Risk mitigation - Human oversight prevents AI errors
  • Compliance - Human accountability maintained
  • Efficiency - Automate high-confidence tasks
  • Continuous improvement - AI learns from human feedback
  • Audit trail - Complete record of AI and human decisions

For AI Systems ​

  • Trust building - Human validation builds confidence
  • Training data - Human decisions improve AI
  • Error detection - Quick identification of AI issues
  • Safe deployment - Gradual automation as confidence grows

For Users ​

  • Augmented capability - AI handles routine, humans handle complex
  • Reduced workload - Only review uncertain cases
  • Better decisions - AI provides analysis, human provides judgment
  • Learning opportunity - Humans learn from AI insights

Implementation Considerations ​

Technical Requirements ​

  • AI system must output confidence scores
  • AI must be able to trigger Capture creation
  • Capture templates must include AI-specific fields
  • Workflows must support AI-human handoff
  • Feedback loop must exist for AI retraining

Organizational Requirements ​

  • Define confidence thresholds
  • Train reviewers on AI capabilities and limitations
  • Establish feedback process
  • Monitor AI performance over time
  • Plan for AI model updates

Process Design ​

  • Clear criteria for when human review needed
  • Defined escalation for AI failures
  • Regular AI performance reviews
  • Adjustment of automation thresholds
  • Continuous improvement mindset

Best Practices ​

Start Conservative ​

  • Begin with low automation threshold
  • Review more cases initially
  • Build confidence in AI performance
  • Gradually increase automation

Measure Everything ​

  • Track AI accuracy
  • Monitor human override rates
  • Measure time savings
  • Calculate cost-benefit
  • Adjust based on data

Train Reviewers ​

  • Explain AI capabilities
  • Clarify AI limitations
  • Teach how to interpret confidence scores
  • Emphasize importance of feedback
  • Update training as AI evolves

Close the Loop ​

  • Ensure human decisions feed back to AI
  • Regular AI model retraining
  • Monitor for model drift
  • Validate AI improvements
  • Communicate changes to users

Future Directions ​

Increasing Automation ​

As AI improves:

  • Higher percentage of automated decisions
  • Human review for edge cases only
  • Faster processing times
  • Lower costs

Smarter Routing ​

Advanced AI integration:

  • Context-aware confidence scoring
  • Automatic expert assignment
  • Predictive review time estimates
  • Intelligent workload balancing

Continuous Learning ​

Next-generation systems:

  • Real-time AI learning from human decisions
  • Adaptive confidence thresholds
  • Personalized AI models per user/domain
  • Explainable AI reasoning

Next Steps ​

Explore related topics:

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