AI now writes or accelerates at least 41% of the code at 65% of organizations, and 63% of teams already run AI agents in production. Confidence is running ahead of evidence. 46% of teams have shipped AI-generated code that later failed in production, and 48% of APIs drift from tested behavior within 90 days. Teams pay for it in rework, outages, and missed release dates, and API incidents cost an average of $700K a year, reaching $1.8M for the hardest hit.

In this fireside chat, analyst Mitch Ashley (Futurum Group, host of DevOps Chats) and Justin Collier - Vice President, Product Management, SmartBear, explore how to keep application integrity intact as AI multiplies how much software you ship and how fast. They'll cover where production confidence breaks today: specs AI builds from before anyone validates them, mocks that miss how real services behave, and load limits that only show up in production. Then they'll lay out a practical path to faster releases with fewer production surprises, grounded in results teams are already seeing.

Key Takeaways:

• How to spot where AI development speed is eroding your API quality

• What it actually costs when API problems reach production instead of getting caught before it

• How contract testing and service virtualization give you realistic dependencies, closing the gap behind 40% of production incidents

• A 90-day roadmap for functional, performance, contract, and virtualization testing that scales with AI generated code instead of becoming the bottleneck

• What it looks like in practice, from companies cutting a 100-test run from 6 hours to 20 seconds to creating tests 80% faster