Less Context, Better Agents: Efficient Context Engineering for Long-Horizon Tool-Using LLM Agents

A recent study on large language models (LLMs) highlights the challenges faced by autonomous agents in enterprise workflows, particularly regarding context overflow and high inference costs. The research evaluated various configurations of GPT-5 in the context of automated expense itemization within Microsoft Dynamics 365 Finance and Operations, revealing significant differences in completion rates based on context management strategies.

The findings indicate that while retaining full conversation history improves itemization completion to 71%, it incurs substantial resource costs. In contrast, pruning context to the last five tool calls enhances completion to 79% with reduced token usage, suggesting a more efficient approach for enterprises.

This development underscores a growing trend in AI research focusing on optimizing LLM performance through context engineering and user intent analysis. As organizations increasingly rely on AI for complex tasks, the balance between context retention and operational efficiency will be crucial, reflecting broader discussions on the capabilities and limitations of LLMs in various applications.