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Building an AI agent is becoming easier. But proving it can be trusted with a real business process is not. Enterprises need a reliable way to test AI agents and their workflows before trusting them with mission-critical quality software testing and assurance tasks. Today, we're excited to introduce Test Data Agent: a new agentic infrastructure capability designed to create the realistic data, business context, and system states enterprises need to safely validate AI agents before full production deployment. Test Data Agent provides production-faithful environments in which teams can: ✅ Identify the business entities, records, relationships, and system states a test requires ✅ Generate, mask, or subset production-representative data ✅ Preserve referential integrity, statistical characteristics, and business rules across interconnected systems ✅ Continuously retest agents as models, instructions, applications, and data evolve The Test Data Agent is one step forward for agent testing, but a giant leap in the race to make Agentic QA enterprise-ready. Learn more: https://lnkd.in/emzuvGfP//?utm_source=linkedin&utm_medium=paid&utm_id=1202162434&utm_campaign=PostBoosts&hsa_acc=515975322&hsa_cam=1202162434&hsa_grp=883050574&hsa_ad=1514781254&hsa_net=linkedin&hsa_ver=3 See the link in the comments to register early access interest. #EnterpriseAI #AIAgents #TestData #AIInfrastructure #SAP #AgenticQA #AIEngineering #TricentisTransform #SAPTDM #TestDataAutomation #TestDataManagement

Coverage, compliance, velocity, and agent readiness all depend on one thing: the right data, in the right shape, at the right time, governed and auditable. That's the data layer, and it's the one factor sitting underneath every SAP testing initiative right now, whether teams treat it that way or not.

A real SAP process rarely lives in one system. It moves through S/4HANA, Ariba, Salesforce, sometimes a database or file store along the way. Prepare test data for each one in isolation, and the integration tests break — one of the most consistent causes of late-stage failure in SAP programs.

Manual test data breaks in four compounding ways: - tribal knowledge walks out the door when one person leaves, - annual refreshes leave you testing against stale data for most of the year, - production data only shows what already happened (not the edge cases you need to test), - and full SAP copies run into terabytes and days to provision. For agentic systems, stale data doesn't just slow testing down. It trains agents toward the wrong behavior before you ever go live.

5 things we learned from our webinar with Paul Kurchina and Eesha Gholap on SAP test data and the Autonomous Enterprise. Most teams can answer the first one without thinking. Fewer can answer all of them. Swipe through, then watch the full session recording via the link below. #SAPTesting #AgenticAI #S4HANA #TestDataManagement #Synthesized #SAPCommunity #AutonomousEnterprise

Your S/4HANA testing looks complete, but does your data reflect real-world scenarios? Most defects come from unrealistic, outdated, or incomplete test data.