Closing the AI Software Quality Gap

Your AI agent can write code, call APIs, and chain together multi-step workflows. But can you prove it works—not once, but reliably, across hundreds of runs, against every edge case and failure mode it will encounter in production? To do this, you’ll need some serious testing. As applications expand from human-coded to AI-augmented to AI-generated, the volume and velocity of code increasingly outpace teams' ability to validate it. As a result, 60% of organizations surveyed by SmartBear report experiencing quality issues in the last year.

This is where application integrity becomes essential. To ship on evidence, not assumptions, requires traceability and measurable confidence in what was tested to keep pace with AI code generation: connecting test intent to execution and outcomes, maintaining auditable evidence, and governing releases through real data and a centralized system of record.

Read this 2026 industry study to understand how teams are closing the AI software quality gap and how testing teams can maintain velocity without sacrificing application stability.

Key takeaways from the report include:

• Strategies to continuously capture, govern, and connect test intent, execution, and outcomes at scale.

• How to deliver software you trust at AI speed by resolving critical testing and deployment bottlenecks.

• Frameworks for requirements-to-test traceability and real-time audit trails using SmartBear Zephyr as your Jira-native testing system of record for confident release decisions.