mcp-agent-graph

### MCP Agent Graph Workflow: Automated Customer Onboarding

**Workflow Overview:**
This graph automates the end-to-end onboarding process for new SaaS customers, reducing manual effort by 80% and cutting average onboarding time from 3 days to 4 hours. The system consists of 5 specialized agents that collaborate via a shared context graph, with long-term memory for tracking customer progress and escalation paths.

**Agent Roles & Logic:**
1. **Intake Agent** (Trigger: New customer signup via Stripe webhook)
   - Validates email domain and company size (B2B vs. B2C)
   - Assigns risk score based on industry (e.g., finance = high risk)
   - Creates a customer record in Notion with initial metadata
   - *Decision Logic:* Routes to "Fast Track" if risk score < 3, else to "Validation Queue"

2. **Validation Agent** (Trigger: Customer record created in Notion)
   - Checks for duplicate accounts using Clearbit enrichment
   - Verifies billing address against IP geolocation
   - Flags suspicious activity (e.g., VPN usage, disposable email)
   - *Decision Logic:* Approves if all checks pass, else escalates to "Fraud Review" or sends a Slack alert to the fraud team.

3. **Provisioning Agent** (Trigger: Validation agent approval)
   - Creates AWS account via Terraform (if enterprise) or Stripe customer (if SMB)
   - Sets up SSO integration using Okta API
   - Generates welcome email sequence via HubSpot
   - Updates customer record in Notion with provisioning status

4. **Education Agent** (Trigger: Provisioning agent completion)
   - Sends personalized onboarding checklist via Intercom
   - Tracks email opens and link clicks to identify drop-off points
   - Triggers "Check-In" agent if no activity after 24 hours

5. **Escalation Agent** (Trigger: Validation failure or fraud alert)
   - Sends email to customer with next steps (e.g., document submission)
   - Creates Jira ticket for manual review with priority based on risk score
   - Logs all interactions in the customer's shared context graph for future reference

**MCP Configuration Code:**
```json
{
  "graph_name": "Customer Onboarding Automation",
  "agents": [
    {
      "name": "intake_agent",
      "role": "Processes new signups and assigns risk scores",
      "tools": ["stripe_webhook_parser", "notion_api", "clearbit_enrichment"],
      "decision_logic": {
        "fast_track": "risk_score < 3",
        "validation_queue": "risk_score >= 3"
      }
    },
    {
      "name": "validation_agent",
      "role": "Validates customer data and flags fraud",
      "tools": ["clearbit_api", "ip_geolocation", "slack_webhook"],
      "escalation_paths": {
        "fraud_review": "suspicious_activity = true",
        "manual_review": "duplicate_account = true"
      }
    }
  ],
  "triggers": [
    {
      "type": "webhook",
      "source": "stripe",
      "event": "customer.created",
      "target_agent": "intake_agent"
    }
  ],
  "shared_context": ["customer_record", "risk_score", "fraud_flags"]
}
```

**Visualization:**
The workflow is rendered as a directed graph where nodes represent agents and edges represent triggers/conditions. The graph shows parallel paths for high-risk vs. low-risk customers, with clear handoff points between agents. A live dashboard in MCP Agent Graph displays real-time metrics like average onboarding time and escalation rates.

**Outcome:**
- **Time Saved:** 12 hours/week for the onboarding team
- **Error Reduction:** 95% fewer manual data entry errors
- **Customer Satisfaction:** 20% faster time-to-value for new users