This paper presents a QA and assurance framework for enterprises deploying Agentic AI in critical workflows. It argues that testing agentic systems is not only a technical activity but also a governance and business-risk function because these systems make decisions, coordinate actions, learn from memory, and affect revenue, compliance, safety, and trust. The document explains why testing is harder than in traditional software or even RAG systems: outputs are non-deterministic, decision paths are opaque, tool integrations introduce failure points, goals may be misinterpreted, and unsafe actions can have real-world consequences. It recommends a rebalanced test pyramid spanning unit tests, AI component tests, AI integration tests, and end-to-end agent tests. The overall message is that assur

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