Smart TVs and OTT devices have become the primary gateway to broadcast and streaming content, yet they present an increasingly complex QA environment. Native applications run across heterogeneous operating systems (Tizen, webOS, Fire OS, Roku OS, Android TV or Google TV, RDK) and render highly dynamic interfaces that include personalized rails, rotating promotions, contextual backdrops, and variable ad driven placements. Traditional scripted and model-based testing methods remain valuable and widely used, yet they can become difficult to maintain as device platforms, app versions, and personalized interfaces evolve on a weekly basis.

This paper presents a new agentic, multi agent AI framework designed to autonomously execute robust QA workflows on Smart TVs and any video devices in full black box conditions. Instead of depending on fixed navigation routes or pre mapped interfaces, the system uses AI that visually interprets the screen, selects actions based on the testing goal, and adjusts its behavior dynamically, enabling navigation that mirrors human interactions.

The paper details the underlying mechanisms that enable:

· Zero shot UI adaptation using VLMs and specialized perception modules to recognize semantic elements such as service logos, promoted content, play controls, install prompts, and ad transitions.

· Multi agent orchestration where Designer, Planner, Runner, and Analyst agents work together to transform a high-level test goal, such as “Open the ABC News live stream and verify playback,” into an executable plan that remains resilient to layout changes, cross language interfaces, and dynamic content insertion.

· Perceptual verification combining semantic checks with video and audio perception modules to validate playback integrity, ad stitching, live latency behaviors, and QoE impairments without reference streams.

· Deterministic reproducibility using techniques that bound agent exploration and ensure that autonomous executions remain traceable, repeatable, and suitable for broadcast engineering environments.

· Scalability across OS ecosystems with empirical results demonstrating reliability across Smart TV platforms, operator STBs, and regional UI variations.

Field evaluations show that agentic workflows reduce test maintenance by approximately 80 percent, maintain robustness despite UI reflows and dynamic content, and significantly expand the range of QA tasks that can be automated. These tasks include app discovery, deep navigation, FAST channel validation, dynamic ad insertion checks, and cross language playback verification.

For broadcast and streaming engineering teams, this work outlines a practical and standardized path for integrating agentic AI into QA and monitoring workflows bridging the gap between traditional script and fully autonomous, goal-oriented device validation in multi-screen environments.