Professional contribution / Recent work
Agent-assisted testing
Designing a testing platform that combines repeatable regression checks with bounded browser agents and reviewable evidence.
Making agent output useful
Browser agents can help explore an application, but a successful interaction is not automatically a meaningful test. A testing system still needs a defined target, an expected result, and evidence that someone can inspect when the run fails.
I designed a platform for agent-assisted end-to-end testing and coordinate its implementation with junior engineers and interns. The work is in development. My contribution centers on platform design, the boundary between agent behavior and test decisions, and the structure of the evidence produced by a run.
My contribution
I worked on combining repeatable browser regressions with bounded agent exploration. The goal is to let agents contribute useful observations while keeping test outcomes grounded in explicit checks. Playwright and browser tooling provide the execution surface; the platform around them gives a run a clear scope and produces reviewable results.
I defined responsibilities across the workflow and helped turn the design into implementable pieces. That included clarifying what agents could attempt, how results should be represented, and which decisions needed deterministic checks or human review.
Delivery coordination has been part of the work as well. I help order dependencies, clarify component boundaries, and mentor engineers implementing the platform. That experience has reinforced how much a usable design depends on making the next person's task understandable.
What I take from it
AI-assisted testing is an evaluation problem as much as an automation problem. A run can complete without proving that the application behaved correctly. Evidence needs to connect an action to an expectation and make unexpected behavior easy to investigate.
I apply the same principle when evaluating the testing system itself. Generated reports and successful browser actions are useful intermediate results. Confidence in a regression suite comes from repeatability, relevant assertions, and observed behavior over time. That is the standard guiding this work as it develops.