How AI improves chaos engineering for better software testing

As production environments grow increasingly complex, traditional observability and testing approaches fall short in identifying latent failures. Chaos Engineering addresses this gap by injecting controlled faults to expose weaknesses before they impact users. Yet many teams struggle with experiment design, prioritization, and operational integration.

In this webinar, experts from Harness discuss how generative AI can augment chaos engineering workflows, enabling faster root cause identification, intelligent experiment selection, and more efficient feedback loops. And they explore how Harness is applying AI/ML techniques to drive decision automation, reduce toil, and align chaos practices with real-world reliability objectives.

What You'll Learn:

• Key challenges engineers face when adopting chaos engineering practices
• How to identify, prioritize, and execute critical chaos experiments for immediate system improvements
• The pivotal role of AI in simplifying chaos engineering and driving actionable insights
• Innovative features in the Harness Chaos Engineering product that transform chaos adoption for reliability-focused teams

Whether you’re building a chaos program from scratch or optimizing an existing one, this webinar will offer actionable insights to elevate your reliability engineering efforts.