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QA is a complex task. Diagnosing failures as intentional change to the product, simple flakes, or real bugs requires vast amounts of context and specialized reasoning. Too much for a single agent to handle.

QA Wolf’s multi-agent model splits tasks among multiple prompts, overseen by an Orchestrator agent, to improve speed and accuracy; which in turn accelerates QA cycles and releases.

Join QA Wolf’s Director of AI Nishant Shukla and host Caleb Masters for a practical breakdown of how we keep 80%+ test coverage stable with human-in-the-loop multi-agent AI.

In this webinar, you’ll learn:

Why single-agent systems hit a ceiling on real E2E test suites.
Three core principles for building scalable, resilient AI agents.
How coordination between agents cuts flake debt and locks in predictable coverage.

See how multi-agent AI keeps test maintenance moving — without burning time and budget.