Deployment

Overview

After the assessment is complete and you've reviewed the recommendations, Friender can architect and deploy purpose-built AI agents to address identified bottlenecks. Each agent is custom-built for your specific operational context.

Deployment is optional — the assessment report is valuable on its own. Many organizations use it as a roadmap for internal AI initiatives.

Deployment Process

Step 1: Agent Design

Based on the assessment recommendations, our solutions architects design an agent specification for each approved opportunity. The spec includes: input data sources, decision logic, output actions, guardrails, and escalation paths.

Step 2: Sandbox Testing

Each agent is deployed first in a sandbox environment that mirrors your production tools. The agent processes historical data to validate behavior before any live deployment. You review the sandbox results and approve or request adjustments.

Step 3: Staged Rollout

Live deployment follows a staged approach:

1. Shadow mode (Week 1): Agent runs alongside existing processes but doesn't take action. Outputs are logged for review.
2. Assisted mode (Weeks 2–3): Agent suggests actions to designated team members who approve or reject each suggestion.
3. Autonomous mode (Week 4+): Agent operates independently within defined guardrails. Exceptions are escalated to human reviewers.

Step 4: Monitoring & Optimization

Post-deployment, agents are continuously monitored against the KPIs identified during assessment. Performance dashboards show actual vs. projected impact, and agent behavior is tuned based on real-world results.

Configuration

{
  "type": "deployment",
  "agentId": "agent_triage_001",
  "config": {
    "mode": "shadow",
    "guardrails": {
      "maxActionsPerHour": 50,
      "requireApproval": ["ticket_reassignment", "priority_change"],
      "escalateOn": ["confidence_below_70", "new_pattern_detected"]
    },
    "notifications": {
      "channel": "#ai-agent-alerts",
      "alertOn": ["error", "escalation", "guardrail_triggered"]
    },
    "schedule": {
      "activeHours": "09:00-18:00",
      "timezone": "America/New_York",
      "activeDays": ["Mon", "Tue", "Wed", "Thu", "Fri"]
    }
  }
}

Guardrails

Every deployed agent operates within configurable guardrails that prevent unintended actions:

• Action rate limits: Maximum actions per hour/day to prevent runaway behavior
• Approval gates: Specific action types that always require human approval
• Confidence thresholds: Actions below a confidence threshold are escalated
• Rollback triggers: Automatic pause if error rate exceeds threshold
• Scope boundaries: Agents can only interact with explicitly approved tools and data

Monitoring Dashboard

Each deployed agent has a real-time dashboard showing:

• Actions taken in the current period
• Success rate and error breakdown
• Time saved vs. projected impact
• Escalation and approval history
• Configuration change audit log

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