Migration Playbook

Transitioning from Traditional Automation to Agentic AI

Document ID: F2-MIGRATION-PLAYBOOK
Version: 1.0
Category: Implementation Guide

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Executive Summary

Organizations with existing RPA, workflow automation, or rules-based systems can systematically migrate to agentic AI for improved flexibility, reduced maintenance, and better handling of edge cases.

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Migration Assessment Framework

Current State Inventory

Automation Type	Characteristics	Migration Complexity	Recommended Paradigm
RPA (Robotic Process Automation)	Screen scraping, fixed workflows	Medium	VE (similar structure)
Rules Engines	If-then logic, decision tables	Low-Medium	VE or GS+VE
Workflow Systems	Sequential processes, approvals	Medium	VE
ETL Pipelines	Data transformation, scheduling	Low	VE
Chatbots (scripted)	Decision trees, keyword matching	High	LSR or GS
ML Models (narrow)	Classification, prediction	Medium	GS or EP

Migration Readiness Scorecard

Factor	Score (1-5)	Weight
Data availability	_	0.20
Process documentation	_	0.15
Exception handling needs	_	0.20
Compliance requirements	_	0.15
Integration complexity	_	0.15
Stakeholder readiness	_	0.15

Interpretation:

• Score > 4.0: Ready for migration
• Score 3.0-4.0: Address gaps first
• Score < 3.0: Significant preparation needed

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Migration Patterns

Pattern 1: RPA to VE

Before (RPA):

1. Open application
2. Navigate to screen
3. Extract field values
4. Apply business rules
5. Enter data in target
6. Click submit
7. Log completion

After (VE Agent):

protocol: data_transfer
steps:
  - action: extract_source_data
    tool: api_call  # Replace screen scraping
    validation: schema_check
  
  - action: apply_business_rules
    tool: rules_engine
    audit: log_decision_rationale
  
  - action: write_target_data
    tool: api_call
    validation: confirmation_check
    rollback: enabled

Benefits:

• API-based instead of brittle UI automation
• Self-documenting with audit trail
• Handles UI changes gracefully
• Explains decisions for debugging

Pattern 2: Rules Engine to GS+VE

Before (Rules Engine):

IF customer_type = "premium" AND order_value > 1000 THEN
  discount = 15%
ELSE IF customer_type = "premium" THEN
  discount = 10%
ELSE IF order_value > 500 THEN
  discount = 5%
ELSE
  discount = 0%

After (GS+VE Hybrid):

GS Phase: Retrieve customer context
- Purchase history
- Loyalty status
- Current promotions
- Competitor pricing (if available)

VE Phase: Apply pricing protocol
- Protocol: pricing_v2
- Inputs: customer_context, order_details
- Validation: margin_check, approval_thresholds
- Audit: full_decision_trace

Benefits:

• Considers broader context
• Handles edge cases not in rules
• Adapts to new scenarios
• Maintains compliance through VE

Pattern 3: Scripted Chatbot to GS

Before (Decision Tree Chatbot):

User: "What's my order status?"
Bot: [Match "order status"] → Ask for order number
User: "12345"
Bot: [Query database] → Return status

After (GS Agent):

User: "What's my order status?"
Agent: 
  1. Identify user (session context)
  2. Retrieve recent orders (knowledge_search)
  3. If multiple orders, clarify which one
  4. Provide status with context:
     - Current status
     - Expected delivery
     - Any known issues
     - Proactive next steps

Benefits:

• Handles variations naturally
• Provides proactive information
• No maintenance of decision trees
• Better customer experience

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Migration Phases

Phase 1: Shadow Mode (Weeks 1-4)

┌──────────────────────────────────────────────┐
│                   INPUT                       │
└─────────────────────┬────────────────────────┘
                      │
         ┌────────────┼────────────┐
         ▼            ▼            ▼
    ┌─────────┐  ┌─────────┐  ┌─────────┐
    │ Legacy  │  │ Agentic │  │ Compare │
    │ System  │  │   AI    │  │ Results │
    └────┬────┘  └────┬────┘  └────┬────┘
         │            │            │
         ▼            │            ▼
    ┌─────────┐       │       ┌─────────┐
    │ OUTPUT  │       └──────►│  LOG    │
    └─────────┘               └─────────┘

Activities:

• Run agentic AI in parallel with existing system
• Compare outputs without affecting production
• Identify discrepancies and edge cases
• Build confidence in agentic approach

Success Criteria:

• Agreement rate > 95% on standard cases
• Edge cases identified and addressed
• Performance within acceptable range

Phase 2: Pilot Mode (Weeks 5-8)

┌──────────────────────────────────────────────┐
│                   INPUT                       │
└─────────────────────┬────────────────────────┘
                      │
                      ▼
                ┌─────────┐
                │ Router  │
                └────┬────┘
         ┌───────────┼───────────┐
         ▼                       ▼
    ┌─────────┐             ┌─────────┐
    │ Agentic │             │ Legacy  │
    │(10-20%) │             │(80-90%) │
    └────┬────┘             └────┬────┘
         │                       │
         ▼                       ▼
    ┌─────────────────────────────────┐
    │            OUTPUT               │
    └─────────────────────────────────┘

Activities:

• Route 10-20% of traffic to agentic system
• Select low-risk, representative cases
• Monitor closely with rapid rollback capability
• Gather user feedback

Success Criteria:

• Error rate < legacy system
• User satisfaction maintained or improved
• Handling time competitive

Phase 3: Majority Mode (Weeks 9-12)

• Increase agentic traffic to 50-80%
• Legacy handles only flagged exceptions
• Focus on edge case handling
• Build operator confidence

Phase 4: Full Migration (Weeks 13-16)

• Agentic system as primary
• Legacy as fallback only
• Document lessons learned
• Plan legacy decommission

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Risk Mitigation

Common Migration Risks

Risk	Mitigation
Output quality degradation	Shadow mode comparison, quality gates
Performance issues	Load testing, caching strategies
Integration failures	API-first approach, fallback handlers
Compliance gaps	VE paradigm for regulated processes
Operator resistance	Training, gradual transition
Cost overruns	Token budgets, monitoring

Rollback Strategy

class MigrationRouter:
    def route(self, request):
        if self.emergency_rollback:
            return self.legacy_system.process(request)
        
        if self.should_use_agentic(request):
            try:
                result = self.agentic_system.process(request)
                if self.quality_check(result):
                    return result
                else:
                    self.log_quality_issue(request, result)
                    return self.legacy_system.process(request)
            except Exception as e:
                self.log_error(e)
                return self.legacy_system.process(request)
        
        return self.legacy_system.process(request)

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Quick Reference: Migration Decision Matrix

Current System	If Edge Cases < 10%	If Edge Cases 10-30%	If Edge Cases > 30%
RPA	Migrate to VE	Migrate to VE + EP fallback	Evaluate EP hybrid
Rules Engine	Migrate to VE	Migrate to GS + VE	Migrate to EP + VE
Scripted Bot	Migrate to GS	Migrate to GS	Migrate to GS + EP
ML Model	Enhance with GS	Migrate to GS + EP	Redesign with EP

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Document maintained by CODITECT Professional Services