Trainloop is an agentic AI QA automation platform that autonomously generates, executes, and maintains tests for web and mobile applications.

How is Trainloop different from traditional QA automation tools?

Unlike script-based or AI-assisted tools, Trainloop uses fully autonomous AI agents to manage the entire QA lifecycle without manual test maintenance.

How is Trainloop 5x cheaper than legacy tools?

By using agentic reasoning to optimize execution paths and token usage, and eliminating the need for manual script maintenance, we reduce total cost of ownership by up to 80%.

Does Trainloop support web and native mobile testing?

Yes. Trainloop supports modern web applications as well as native Android and iOS mobile apps using unified agentic AI logic.

Is Trainloop suitable for startups and enterprises?

Yes. Trainloop is designed to scale from early-stage startups to large enterprise QA environments with features like SOC 2 compliance and VPC deployment.

What does agentic AI mean in QA testing?

Agentic AI refers to autonomous systems that can independently plan, execute, adapt, and optimize testing activities without human intervention or manual scripting.

Agentic AI vs. Traditional Automation: A Technical Paradigm Shift

The transition from script-based automation to agentic AI represents a paradigm shift in software testing. This comparison highlights the structural differences between these two approaches.

High-Level Comparison Table

Feature	Traditional Automation	Agentic AI QA
Creation	Manual Scripting	Autonomous Discovery
Maintenance	Manual Refactoring	Self-Healing
Flexibility	Rigid / Fragile	Adaptive
Reliability	Susceptible to Flakiness	Logic-Driven Resilience
Expertise	SDET / Framework Knowledge	Domain Context

Maintenance Effort

In traditional frameworks like Selenium or Playwright, maintenance often consumes up to 30% of an engineering team's bandwidth. Agentic AI systems autonomously resolve UI element shifts and API schema updates, significantly lowering this overhead.

Execution Reliability

By using agentic logic rather than brittle selectors, autonomous agents can verify application behavior through vision and state analysis, reducing the false positives common in traditional environments.