The Agent Integration Matrix: Connecting AI to Everything

The Integration Spectrum

Level 0: No AI Access
├── Sensitive credentials
├── Compliance-restricted data
└── No business value from AI access

Level 1: Read-Only Context
├── Reference data (docs, wikis)
├── Historical records
└── Reporting dashboards

Level 2: Read + Summarize
├── Customer communications
├── Meeting transcripts
└── Support tickets

Level 3: Read + Suggest Actions
├── CRM data → "Schedule follow-up"
├── Analytics → "Investigate anomaly"
└── Pipeline → "Update forecast"

Level 4: Read + Write (with approval)
├── Draft emails (human sends)
├── Prepare documents (human reviews)
└── Stage changes (human commits)

Level 5: Autonomous Actions
├── Auto-respond to simple queries
├── Update routine records
└── Trigger workflows

The Decision Framework

Question 1: What's the Risk?

risk_assessment = {
  "financial_impact": {
    "low": "< $1k per error",
    "medium": "$1k - $50k",
    "high": "> $50k"
  },
  "reputational_impact": {
    "low": "Internal only",
    "medium": "Customer-facing, recoverable",
    "high": "Public, lasting damage"
  },
  "reversibility": {
    "easy": "Undo in seconds",
    "medium": "Requires manual correction",
    "hard": "Cannot be undone"
  }
}

// High risk + Hard to reverse = Lower integration level

Question 2: What's the Value?

value_assessment = {
  "frequency": "How often is this needed?",
  "time_saved": "Minutes per occurrence",
  "quality_improvement": "Better outputs with AI?",
  "new_capability": "Enables something impossible before?"
}

// Daily task, 15min each, clear improvement = Worth automating

Question 3: What's the Quality?

quality_requirements = {
  "accuracy_needed": {
    "directional": "Right ballpark (80%+ ok)",
    "precise": "Must be exact (99%+ needed)",
    "perfect": "Zero tolerance (100% required)"
  },
  "ai_capability": {
    "strong": "LLMs excel at this",
    "moderate": "Works with good context",
    "weak": "AI struggles here"
  }
}

// "Perfect" accuracy + "Weak" AI capability = Don't automate

Common Integration Patterns

Pattern 1: Context Provider

// Agent reads, never writes
// Safest integration pattern

MCP Server: "Here's the customer's history"
Agent: Uses context to answer questions
Human: Takes any actions

Use for:
- Documentation
- Historical data
- Reference materials
- Read-only dashboards

Pattern 2: Draft Generator

// Agent writes drafts, human approves

Agent: "Here's a draft email"
Human: Reviews, edits, sends

Use for:
- Customer communications
- Reports and summaries
- Code suggestions
- Content creation

Pattern 3: Action Suggester

// Agent recommends, human executes

Agent: "I recommend scheduling a call with this customer"
Human: [Schedule] [Dismiss] [Modify]

Use for:
- Sales next steps
- Support ticket routing
- Task prioritization
- Alert triage

Pattern 4: Supervised Automation

// Agent acts within bounds, with audit trail

Agent: "I updated the status to 'Resolved'"
System: Logs action, notifies human
Human: Can review and reverse if needed

Use for:
- Status updates
- Data enrichment
- Tagging and categorization
- Routine acknowledgments

Pattern 5: Full Autonomy

// Agent acts independently

Agent: Handles entire workflow
Human: Reviews reports, handles exceptions

Use for:
- Low-stakes, high-volume tasks
- Well-defined, rule-based processes
- Areas where speed matters more than perfection

Integration Priority Matrix

Implementation Checklist

For each integration:

□ Defined access level (0-5)
□ Documented risk assessment
□ Set up audit logging
□ Configured rate limits
□ Established rollback procedures
□ Created human escalation paths
□ Tested edge cases
□ Reviewed with security team
□ Trained users on capabilities and limits

The 80/20 Rule

Most of the value comes from Level 1-2 integrations:

• 80% of value: AI with great context (read-only)
• 15% of value: AI that drafts (with human review)
• 5% of value: Full automation (rarely worth the risk)

Start with read-only context. It's the highest value-to-risk ratio in AI integration.