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When CLAUDE Code explores a large codebase over an extended session, the context window fills up and the model's responses start to degrade. It goes from citing specific classes and line numbers to making vague claims about typical patterns. This happens because the conversation history accumulates verbose tool output, file contents, and search results until the model can no longer hold all the details in its working context. This video teaches you five practical techniques to prevent context degradation and keep your AI agent sharp during large codebase investigations. These are the exact strategies you need to pass Domain 5 of the Claude Certified Architect exam and to run real-world codebase investigations that last hours instead of minutes. What you'll learn: - Why context degrades during extended exploration sessions and how to recognize the signals - Scratchpad files as persistent external memory for key findings - Subagent delegation to keep the main agent's context clean - The /compact command for compressing verbose conversation context - Structured state persistence for crash recovery in multi-hour investigations - The summarize-then-inject pattern for keeping downstream agent context focused Timestamps: 0:00 — Recap: Error Propagation in Multi-Agent Systems 0:35 — Today: Large Codebase Context (Task 5.4) 1:05 — The Context Degradation Problem 2:10 — Scratchpad Files: Persistent External Memory 3:15 — Subagent Delegation for Exploration 4:20 — The /compact Command 5:15 — Structured State Persistence for Crash Recovery 6:20 — Summarizing Between Phases 7:15 — Five Anti-Patterns to Avoid 8:30 — Exam Framework: What You Must Know 9:15 — Summary and Next Part Teaser Keywords: CLAUDE Code context management, large codebase exploration, AI agent context window, scratchpad files, subagent delegation, /compact command, crash recovery, context degradation, Claude Certified Architect exam prep, Domain 5 Task 5.4, AI coding agent best practices, context persistence, multi-phase investigation In Part 30, we'll cover Human Review Workflows — confidence calibration, stratified random sampling, and the human-in-the-loop safety net that catches extraction errors before they reach production. #CLAUDECode #AICertified #ContextManagement #CodebaseExploration #AIAgent #DeveloperTools #ExamPrep #CodingAgent #SoftwareEngineering #AITools #DeveloperProductivity #ClaudeArchitect
