Research Shows Selective Context Pruning and Summarization Boost LLM Agent Performance While Cutting Costs

A new study from arXiv researchers demonstrates that LLM agents handling enterprise workflows achieve significantly better results with less context—achieving 91.6% task completion on expense itemization by retaining only recent tool interactions plus automated summaries, compared to 71% with full history. The optimized approach also reduced token consumption by 63% and runtime by 60%, suggesting that context engineering strategies are critical for deploying reliable autonomous agents in real-world business systems.

Why it matters: As enterprises deploy LLM agents for complex workflows, understanding how to manage context window limitations while maintaining accuracy directly impacts both system reliability and operational costs at scale.