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Lesson 18 of 18

Real-World Case Studies

lesson-03

5 min read

Learn how a development team used AI agents to integrate a complex enterprise ecosystem with multiple legacy systems.

Project Overview

Company: GlobalCorp Financial Services Challenge: Integrate 12 disparate systems into a unified data platform Timeline: 4 months (reduced from 14 months estimate) Team: 6 developers + AI agents Systems: SAP, Salesforce, Oracle DB, custom mainframe, 8 microservices

The Integration Landscape

Integration Requirements:
├── SAP ERP (Financial data)
├── Salesforce CRM (Customer data)
├── Oracle Database (Legacy transactions)
├── IBM Mainframe (Core banking)
├── 5 REST APIs (Modern services)
├── 3 SOAP services (Legacy services)
├── File-based feeds (Daily batches)
└── Real-time event streams (Kafka)

Phase 1: Discovery and Mapping

AI-Powered System Analysis

# Claude Code analyzing API documentation
claude "Analyze these API specifications and create:
1. A unified data model mapping
2. Entity relationship diagrams
3. Data transformation requirements
4. Conflict resolution strategies
5. Integration sequence diagrams"

Generated Integration Map

// AI-generated unified schema
// src/schemas/unified-customer.ts

export interface UnifiedCustomer {
  // Canonical ID (generated)
  id: string;

  // Source system references
  sourceIds: {
    salesforce?: string;    // Salesforce Account ID
    sap?: string;           // SAP Customer Number
    oracle?: string;        // Oracle CUST_ID
    mainframe?: string;     // Mainframe CIF
  };

  // Unified fields with source tracking
  profile: {
    name: string;
    source: 'salesforce' | 'sap';  // Golden source
    lastUpdated: Date;
  };

  financials: {
    creditLimit: number;
    source: 'sap' | 'mainframe';
    lastUpdated: Date;
  };

  // Conflict resolution metadata
  conflicts: ConflictRecord[];

  // Audit trail
  integrationHistory: IntegrationEvent[];
}

// Transformation rules
export const transformationRules: TransformationConfig = {
  customer_name: {
    sources: ['salesforce.Name', 'sap.NAME1', 'oracle.CUSTOMER_NAME'],
    priority: ['salesforce', 'sap', 'oracle'],
    transform: (values) => values.salesforce || values.sap || values.oracle,
    conflictStrategy: 'highest_priority'
  },
  credit_limit: {
    sources: ['sap.CREDIT_LIMIT', 'mainframe.CRLM'],
    priority: ['sap', 'mainframe'],
    transform: (values) => Math.max(values.sap || 0, values.mainframe || 0),
    conflictStrategy: 'maximum_value'
  }
};

Phase 2: Adapter Development

Multi-Agent Adapter Creation

The team assigned different AI agents to build system-specific adapters:

# Agent assignment matrix
adapters:
  salesforce:
    agent: claude-code-1
    complexity: medium
    auth: oauth2
    protocol: rest

  sap:
    agent: cursor-1
    complexity: high
    auth: basic + certificates
    protocol: rfc + odata

  mainframe:
    agent: devin
    complexity: very_high
    auth: custom
    protocol: mq + file

  oracle:
    agent: cursor-2
    complexity: medium
    auth: wallet
    protocol: jdbc

SAP Adapter Example

// src/adapters/sap/customer-adapter.ts
// Generated by Claude Code with human refinement

import { SapRfcClient } from '@anthropic/sap-rfc';
import { UnifiedCustomer, transformationRules } from '@/schemas';

export class SapCustomerAdapter implements ICustomerAdapter {
  private client: SapRfcClient;

  constructor(config: SapConfig) {
    this.client = new SapRfcClient({
      ashost: config.host,
      sysnr: config.systemNumber,
      client: config.client,
      user: config.user,
      passwd: config.password,
      lang: 'EN'
    });
  }

  async fetchCustomer(sapNumber: string): Promise<UnifiedCustomer> {
    // Call SAP RFC function
    const result = await this.client.call('BAPI_CUSTOMER_GETDETAIL2', {
      CUSTOMERNO: sapNumber
    });

    // Transform SAP structure to unified model
    return this.transformToUnified(result);
  }

  async fetchCustomersBatch(
    criteria: SearchCriteria
  ): Promise<UnifiedCustomer[]> {
    // Use SAP OData for bulk queries
    const odataClient = this.createODataClient();

    const response = await odataClient
      .get('/sap/opu/odata/sap/API_BUSINESS_PARTNER/A_Customer')
      .filter(this.buildFilter(criteria))
      .select(['Customer', 'CustomerName', 'CreditLimit'])
      .top(1000)
      .execute();

    return response.value.map(this.transformToUnified);
  }

  private transformToUnified(sapData: SapCustomerData): UnifiedCustomer {
    return {
      id: generateCanonicalId('sap', sapData.CUSTOMERNO),
      sourceIds: {
        sap: sapData.CUSTOMERNO
      },
      profile: {
        name: sapData.NAME1,
        source: 'sap',
        lastUpdated: new Date()
      },
      financials: {
        creditLimit: parseFloat(sapData.CREDIT_LIMIT) || 0,
        source: 'sap',
        lastUpdated: new Date()
      },
      conflicts: [],
      integrationHistory: [{
        timestamp: new Date(),
        source: 'sap',
        action: 'fetch',
        success: true
      }]
    };
  }
}

Mainframe Adapter (Devin-Generated)

// src/adapters/mainframe/customer-adapter.ts
// Generated by Devin with autonomous testing

import { MQClient } from '@/lib/mq';
import { parseEbcdic, formatEbcdic } from '@/lib/ebcdic';

export class MainframeCustomerAdapter implements ICustomerAdapter {
  private mqClient: MQClient;
  private requestQueue: string;
  private responseQueue: string;

  async fetchCustomer(cif: string): Promise<UnifiedCustomer> {
    // Build COBOL-compatible request
    const request = this.buildCicsRequest({
      transactionId: 'CUST',
      operation: 'INQ',
      customerCif: cif.padStart(10, '0')
    });

    // Send to mainframe via MQ
    const correlationId = await this.mqClient.put(
      this.requestQueue,
      formatEbcdic(request)
    );

    // Wait for response with timeout
    const response = await this.mqClient.get(
      this.responseQueue,
      { correlationId, timeout: 30000 }
    );

    // Parse EBCDIC response
    const parsed = parseEbcdic(response, CUSTOMER_COPYBOOK);

    return this.transformToUnified(parsed);
  }

  private buildCicsRequest(params: CicsParams): Buffer {
    // Fixed-length COBOL record format
    const buffer = Buffer.alloc(200);

    buffer.write(params.transactionId, 0, 4);
    buffer.write(params.operation, 4, 3);
    buffer.write(params.customerCif, 7, 10);
    // ... additional fields per copybook

    return buffer;
  }
}

Phase 3: Orchestration Layer

Event-Driven Integration Hub

// src/orchestration/integration-hub.ts

import { Kafka, Consumer, Producer } from 'kafkajs';
import { AdapterRegistry } from '@/adapters';
import { ConflictResolver } from '@/resolution';

export class IntegrationHub {
  private kafka: Kafka;
  private adapters: AdapterRegistry;
  private conflictResolver: ConflictResolver;

  async processCustomerEvent(event: IntegrationEvent) {
    const { sourceSystem, entityId, eventType } = event;

    // Fetch from source system
    const sourceAdapter = this.adapters.get(sourceSystem);
    const sourceData = await sourceAdapter.fetchCustomer(entityId);

    // Fetch from all other systems for comparison
    const allSystemData = await this.fetchFromAllSystems(
      sourceData.sourceIds
    );

    // Resolve conflicts using AI-assisted rules
    const resolved = await this.conflictResolver.resolve(
      allSystemData,
      sourceData
    );

    // Publish unified record
    await this.publishUnifiedRecord(resolved);

    // Sync back to systems if needed
    if (resolved.requiresSync) {
      await this.syncToSystems(resolved);
    }
  }

  private async fetchFromAllSystems(
    sourceIds: SourceIds
  ): Promise<SystemDataMap> {
    const fetchPromises = Object.entries(sourceIds)
      .filter(([_, id]) => id != null)
      .map(async ([system, id]) => {
        const adapter = this.adapters.get(system as SystemType);
        try {
          const data = await adapter.fetchCustomer(id!);
          return [system, { success: true, data }];
        } catch (error) {
          return [system, { success: false, error }];
        }
      });

    const results = await Promise.all(fetchPromises);
    return Object.fromEntries(results);
  }
}

AI-Assisted Conflict Resolution

// src/resolution/ai-resolver.ts

import Anthropic from '@anthropic-ai/sdk';

export class AIConflictResolver {
  private anthropic: Anthropic;

  async resolveComplexConflict(
    conflict: DataConflict
  ): Promise<ResolutionDecision> {
    // For complex conflicts, use AI analysis
    const response = await this.anthropic.messages.create({
      model: 'claude-sonnet-4-20250514',
      max_tokens: 1024,
      messages: [{
        role: 'user',
        content: `Analyze this data conflict and recommend resolution:

Source Systems:
${JSON.stringify(conflict.sourceData, null, 2)}

Conflict Type: ${conflict.type}
Field: ${conflict.field}
Business Rules: ${JSON.stringify(conflict.rules)}

Provide:
1. Recommended resolution
2. Confidence score (0-1)
3. Reasoning
4. Data quality indicators`
      }]
    });

    return this.parseResolutionResponse(response);
  }
}

Phase 4: Testing and Validation

AI-Generated Test Suites

// tests/integration/customer-sync.test.ts
// Generated by Claude Code based on integration requirements

describe('Customer Integration Sync', () => {
  describe('Cross-System Consistency', () => {
    it('should maintain data consistency across SAP and Salesforce', async () => {
      // Create customer in Salesforce
      const sfCustomer = await salesforce.createCustomer({
        Name: 'Test Corp',
        BillingCity: 'New York'
      });

      // Trigger integration
      await integrationHub.syncCustomer(sfCustomer.Id);

      // Verify SAP received the data
      const sapCustomer = await sap.getCustomerBySalesforceId(sfCustomer.Id);

      expect(sapCustomer).toBeDefined();
      expect(sapCustomer.NAME1).toBe('Test Corp');
      expect(sapCustomer.CITY1).toBe('New York');
    });

    it('should resolve conflicts using priority rules', async () => {
      // Set different credit limits in SAP and mainframe
      await sap.updateCreditLimit('CUST001', 50000);
      await mainframe.updateCreditLimit('CUST001', 75000);

      // Trigger reconciliation
      const unified = await integrationHub.reconcileCustomer('CUST001');

      // Should use maximum value strategy
      expect(unified.financials.creditLimit).toBe(75000);
      expect(unified.conflicts).toHaveLength(1);
      expect(unified.conflicts[0].resolution).toBe('maximum_value');
    });
  });

  describe('Error Handling', () => {
    it('should handle mainframe timeout gracefully', async () => {
      // Simulate mainframe delay
      jest.spyOn(mainframeAdapter, 'fetchCustomer')
        .mockImplementation(() => new Promise((_, reject) =>
          setTimeout(() => reject(new Error('Timeout')), 35000)
        ));

      const result = await integrationHub.fetchWithFallback('CUST001');

      // Should return partial data from available systems
      expect(result.sourceIds.mainframe).toBeUndefined();
      expect(result.sourceIds.sap).toBeDefined();
      expect(result.partialData).toBe(true);
    });
  });
});

Results and Impact

Project Metrics

Metric Traditional With AI Agents
Timeline 14 months 4 months
Developer Hours 12,000 4,200
Adapters Built 12 12
Test Coverage 45% 89%
Post-Launch Issues ~50 8

Integration Performance

Daily Transaction Volume: 2.4M records
Average Latency: 145ms (real-time)
Batch Processing: 500K records/hour
Error Rate: 0.02%
Conflict Resolution: 99.7% automated

Key Learnings

Success Factors

  1. Parallel Adapter Development: Each AI agent worked on different system
  2. AI Conflict Resolution: Handled edge cases humans would miss
  3. Automated Testing: AI generated comprehensive test scenarios
  4. Documentation: AI maintained up-to-date integration docs

Challenges and Solutions

Challenge Solution
Legacy protocol complexity Devin for autonomous exploration
Data format inconsistencies AI-generated transformation rules
Performance bottlenecks AI-optimized query patterns
Security requirements Human review of all auth code

Architecture Principles Established

## Integration Guidelines

1. **Canonical Data Model**: All systems map to unified schema
2. **Event-Driven Sync**: Changes propagate via Kafka events
3. **Conflict Resolution**: Automated with AI fallback
4. **Audit Trail**: Every transformation logged
5. **Graceful Degradation**: Partial data on system failures
6. **Human Escalation**: Complex conflicts flag for review

:::

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Module 6: Real-World Case Studies

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