Interactive agents increasingly fail on long task processes involving user turns, tool calls, and evolving environment contexts. Existing agent debugging methods provide useful trace-level diagnosis, but generally return explanations for individual failed traces rather than recurring, fine-grained action-context patterns across runs. To offer such corpus-level analyses, we present AgentTAC (Temporal Action-Context), an automated framework for mining erroneous and generalizable temporal sequences of action and context properties from agent traces. AgentTAC first localizes all distinct errors in each involved agent run. Then, it constructs and maintains a dynamic pool of attributes over the involved tasks, actions, and contexts to summarize their intrinsic properties and contextual relationships. Finally, it extracts temporally contiguous attribute patterns anchored at the localized errors. The resulting error patterns describe failures as interpretable sequential combinations of context and actions, such as making decisions under a missing-constraint context or taking an action after an incompatible prior decision. Extensive evaluations of AgentTAC on multiple benchmarks and agents validate its capabilities in error localization and error pattern mining, and provide preliminary evidence for error-pattern-driven test-time repair.