LLM agents lose tool output before model processing, impacting accuracy

A new paper from Igor Shavlak and colleagues highlights a critical issue in LLM agent harnesses: significant information loss occurs between a tool's output and when the model actually processes it. Their research found that a substantial portion of tool results were truncated or lost entirely due to the harness's serialization and budget-cutting processes, even when the context window was largely unused. This loss, which disproportionately affected information appearing later in the output, led to reduced accuracy and increased computational costs for the agent, often without being visible in standard performance metrics. AI

IMPACT Highlights a critical, often invisible, failure mode in LLM agents that can lead to reduced accuracy and increased costs, necessitating new evaluation metrics.

RANK_REASON The cluster is based on a research paper detailing findings about LLM agent harnesses. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

• arXiv 2610.36114
• Context Is a Pipeline, Not a Window: Information Attrition in LLM Agent Harnesses
• Igor Shavlak
• JSON
• Linux
• LLM Agent Harnesses

• paper
• product

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