AI is reshaping every aspect of software development. While many QE teams are still exploring basic automation, forward-thinking leaders are building autonomous testing systems that operate continuously and adapt intelligently.

The organizations implementing agentic AI in quality engineering today are establishing competitive advantages that will compound over the next several years.

Why This Matters to You

Strategic positioning: Transform your team from reactive testers to proactive quality strategists who guide AI systems rather than compete with them.

Resource optimization: Deploy your team’s expertise on high-value activities while AI handles repetitive execution tasks.

Leadership credibility: Present clear implementation plans that demonstrate your team’s evolution alongside broader organizational AI initiatives.

Future readiness: Build capabilities that will define quality engineering excellence in the coming decade.

What You’ll Learn

• Implementation framework: The proven 5-phase approach for transitioning from traditional automation to agentic systems
• Technology roadmap: Specific tools, platforms, and integrations that enable autonomous testing
• Team transformation: How to evolve roles and responsibilities as AI capabilities expand
• Business case development: ROI models and justification frameworks for securing implementation resources
• Risk mitigation: Common pitfalls in AI testing adoption and how to avoid them

Real-World Application

See how autonomous testing workflows handle complex scenarios: from code commit to comprehensive testing across multiple services, with intelligent failure analysis and automated resolution—all while your team focuses on strategic quality initiatives.