As Large Language Models (LLMs) evolve into autonomous Deep Search and Deep Research Agents capable of executing long-horizon, multi-step tasks, evaluating their reliability becomes increasingly critical. However, existing evaluation paradigms predominantly focus on final task outcomes, leaving a significant gap in understanding the intermediate execution traces, where agents are highly susceptible to various errors. To address this critical gap, we introduce a comprehensive methodology and analytical framework designed specifically to diagnose errors in intermediate workflows. Building upon this framework, we establish a structured taxonomy that categorizes these vulnerabilities into two primary dimensions: System-Level Errors and Task-Level Errors. Through a systematic examination of step-by-step execution traces and agent-environment interactions, we reveal how these underlying errors affect overall performance. Finally, through empirical experiments and systematic analysis of the identified error types, we provide actionable design guidelines, aiming to enhance the robustness, reasoning capabilities, and reliability of Deep Search and Deep Research agents.

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