Testing and release validation have quietly become the bottleneck of modern software delivery. As AI accelerates how fast code is written and legacy systems are modernized, the slowest and most manual part of the lifecycle is increasingly the work of proving that a release does what it should. Test plans, requirements traceability, and regression coverage are expensive to produce by hand, they drift out of date between releases, and in federal environments they are not optional, because documented test coverage and traceability are prerequisites for authorization. The result is a widening gap between delivery speed and the validation evidence that mission systems require.

This session examines how agentic AI can close that gap by generating release-validation artifacts directly from authoritative sources. It walks an end-to-end workflow in which AI ingests application definitions, requirements documents, existing test cases, or agile user stories; computes a semantic difference between releases so that validation focuses on what actually changed; and generates a complete test plan, a requirements traceability matrix with live coverage metrics, and detailed test cases, all reviewed and approved by people. From there, browser-use agents execute and record newly introduced test cases and turn them into automated tests in Selenium, Playwright, or Cypress, exportable as a complete project. Testers can monitor application and server activity in real time and even chat with the codebase to understand its behavior, while a secure credential store, runtime-only secret handling, and a complete audit log keep the workflow safe and accountable.

This is not a concept on a slide. A multi-month pilot is underway with Air Force Civil Engineering. For federal missions the decisive advantage is the evidence base: a generated traceability matrix, documented coverage, and an immutable audit trail map directly onto the test and accreditation artifacts that the Risk Management Framework and security reviews demand, which makes both authorization and reauthorization faster and better supported. The session is candid about where human test engineers remain essential and how to keep AI-generated tests trustworthy.

The session includes a live demonstration of the platform, moving from ingested requirements through a generated traceability matrix and test cases to runnable automated tests.

What you will learn:

• How agentic AI generates test plans, requirements traceability matrices, and detailed test cases from requirements, user stories, or application definitions

• How semantic release diffs focus validation effort on what actually changed between versions

• How browser-use agents execute and record test cases and generate automation in Selenium, Playwright, or Cypress

• How traceable test evidence and an audit trail map to RMF and accreditation requirements

• Where human test engineers remain essential and how to keep AI-generated tests trustworthy

Who should attend: Test and evaluation leads, QA and automation engineers, DevSecOps teams, ISSMs and security control assessors, and program managers responsible for release validation, quality, and authorization.