Agentic AI & autonomous QA platforms describe a new class of testing and automation tools that combine generative models, action-capable agents, and test automation to create, execute, and maintain real-world quality assurance at scale. Rather than only generating test code, these systems observe application interfaces, perform multistep workflows, produce end-to-end and visual tests, surface evidence (video, logs), and self-heal when UI or data changes break scenarios. The topic is timely in 2026 because software complexity and delivery cadence continue to increase, driving demand for continuous, low-friction QA that keeps pace with CI/CD. Key trends include agentic models that can act inside UIs (Adept/ACT-1 style), no-code/low-code agent builders for broader team adoption (StackAI, MindStudio), quality-first automation tied to the SDLC (Qodo/Codium), enterprise orchestration and governance for multi-agent workflows (Kore.ai), and domain-specific QA for contact centers and voice agents (Observe.AI). Bugster exemplifies the emergent QA-agent pattern by creating real-browser E2E and visual tests with self-healing and recorded evidence to reduce flakiness. Adoption considerations include integrating agents into existing pipelines, ensuring observability and auditability, managing model-driven errors (hallucinations), and applying governance for data and decision trails. For teams evaluating these platforms, important differentiators are interface-level action capability, test-maintenance automation, enterprise controls and observability, ease of authoring (no-code vs pro-code), and how well the platform ties automated tests back into development workflows. Together these capabilities represent a pragmatic shift from static test suites to continuously adaptive, agent-driven QA.