Option D is correct because the problem is not insufficient nominal context capacity; it is degraded signal quality as the working context grows. Anthropic’s context-window guidance explicitly states that more context is not automatically better. As conversations expand, recall and accuracy can degrade through “context rot,” so long-running agents need active context management rather than simply retaining every prior tool result.

A good pattern preserves the active task state while compacting, summarizing, or pruning stale intermediate outputs. Anthropic documents server-side compaction for long-running conversations and context-editing/pruning approaches that clear old tool results when they no longer contribute useful information. This keeps high-value constraints and conclusions visible without carrying every verbose response forward.

Option A changes frameworks without fixing the architectural cause. Option B may reduce call count but also creates larger multi-purpose tools and does not guarantee better context quality. Option C increases capacity while preserving the same low-signal accumulation, so the relevant facts can remain buried.

Therefore, D best implements context engineering for agentic workloads. Relevant Study Guide topics: context engineering, context rot, compaction, tool-result pruning, long-running agents, state preservation, and context-window optimization.

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