Long-Running AI Agents Accumulate Context Debt
7.4 relevance
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Directly addresses AI agent context debt, highly relevant and novel.
Summary
Long-running AI agents, like a monthly operating review agent, accumulate 'context debt' as raw query results and discarded hypotheses crowd the prompt, degrading relevance. The article prescribes four storage roles—working context, durable task state, evidence storage, deliverable state—to move material out of the prompt, and advocates structured checkpoints (e.g., JSON with accepted/rejected findings) that preserve decisions rather than generic summaries. Subtask isolation and progressively loaded instructions enforce these boundaries but require a state model.