진화하는 에이전틱 테스트 - 사용자 참조 요약의 맥락에서 Tricentis 활용

This IDC Perspective is one in a series of IDC documents that examine agentic testing and leveraging both AI and ML for automated software quality (ASQ) solutions, providing vendor analysis and customer reference context for technology adoption and end-user buyer decision-making. We discuss Tricentis' Agentic Quality Engineering platform and AI Workspace, including capabilities launched or updated in 1H26. Tricentis' portfolio breadth across test management, model-based automation, performance testing, and quality intelligence - combined with its focus on ERP and packaged application coverage - positions the company to address pressing demand for coordinated, governed AI agents for software quality. We discuss Tricentis relative to trends in user adoption demand for combined agentic testing, risk-based quality intelligence, and scaled enterprise execution. We do so in the context of a volatile economy with tariffs, ongoing flexible work, geopolitical upheaval, and unpredictability evolving into 2H26 and 2027. We also include summaries of customer references from a global information services and technology company and the healthcare information services arm of a global data and technology company."IDC sees adoption of AI and ML for testing, including agentic testing for AI assurance, enabling dynamic execution for high-quality software, innovation, and adaptive responsiveness to dynamically changing environments. Our research shows that around 91% of organizations are piloting, using, or expanding the use of AI for software testing. Key areas of focus include test process improvement insights, visual testing, test case creation, root cause analysis, test prioritization, and synthetic test data. As code creation increases rapidly with the use of code assistants, the role played by agentic testing increases dramatically. Organizations prioritize code quality and consistency over development speed as the primary goal for agentic AI tools, and trust in agentic AI is highest for code testing, where outcomes are observable and verifiable. It is in part due to these trends that IDC has chosen to prioritize this area as one of several areas of focus for a series of end-user-oriented updated vendor analyses that include customer summaries." - Melinda Ballou, research director, AI Assurance, ALM, Quality, and Portfolio Strategies, IDC

Executive Snapshot

• Key takeaways
• Recommended actions

Situation Overview

• Drivers for agentic testing to increase efficiency
• AI, agentic, and machine learning support for AI assurance
• Tricentis Agentic Quality Engineering platform overview
  • Tricentis AI Workspace and Agentic Quality Engineering platform
  • Strategic direction: June 2026 update
  • Key product capabilities and differentiators

IDC's Point of View

• User reference summaries
  • Global information services and technology company evolves toward agentic quality engineering
    • Organization overview
    • Testing challenges and strategic context
    • Adoption of Tricentis products
    • Agentic quality engineering strategy
    • Quantified benefits and outcomes
    • Organizational and cultural considerations
    • Advice to peer organizations
  • Healthcare information services arm company drives automation maturity and AI adoption with Tricentis
    • Organization overview
    • Testing challenges and strategic context
    • Adoption of Tricentis products
    • Agentic quality engineering strategy and AI adoption
    • Quantified benefits and outcomes
    • Organizational and cultural considerations
    • Advice to peer organizations
• Challenges and opportunities

Advice for the Technology Buyer

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• Related research
• Synopsis