AI has already moved beyond text generation. Modern agents can browse the internet, read documents, call APIs, query databases, and coordinate numerous actions between tools and services. They are expected to do more than simply provide a single nebulous answer. In real-world systems, agents evaluate the quality of their own results, independently identify errors, and learn. This capacity for reflection and adaptation distinguishes deep agent systems from the simple, one-off interactions of language models based on the 'one question, one answer' principle. A single answer implies incomplete reasoning, a lack of context, unclear instructions, and contradictory constraints. Rather than treating the generated results as final, the agent verifies them by asking questions: