Less Context, Better Agents: Efficient Context Engineering for Long-Horizon Tool-Using LLM Agents
Quick Answer
This paper shows that In a study on automated expense itemization using GPT-5 in Microsoft Dynamics 365, selective context pruning and summarization improved itemization accuracy to 91.6% while reducing token usage and runtime significantly.
Quick Take
The best configuration used only 553,374 tokens and took 5.79 hours, demonstrating that efficient can enhance both reliability and efficiency in enterprise workflows.
Key Points
- No-user-model baseline achieved only 8.0% complete itemization.
- Full-context retention improved completion to 71.0%, consuming 1,480,996 tokens.
- Pruning to the last 5 tool calls raised completion to 79.0% with 535,274 tokens.
- Summarization led to the best result: 91.6% complete itemization with 553,374 tokens.
- Results indicate selective retention enhances reliability and efficiency in enterprise workflows.
Paper Resources
Source Excerpt
deployed as autonomous agents for enterprise workflows face a key challenge: verbose tool responses from enterprise systems can cause context overflow, stale-state errors, and high inference cost. We study this problem in automated expense itemization in Microsoft Dynamics 365 Finance and Operations using tools. We evaluate four GPT-5 configurations on a 50-task hotel expense benchmark: no user model, full conversation history, context pruned to the l
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →AINTMA: Agentic AI Architecture for Autonomous Test Management with Generative Intelligence, Secure Cloud Communication and Adaptive Quality Analytics
AINTMA, an autonomous test management architecture utilizing six specialized AI agents, achieves 88.4% test prioritization accuracy and reduces defect escape rates from 8.3% to 2.1%. The system demonstrates a 340% ROI within nine months, showcasing the potential of agentic AI in enhancing software quality management in cloud environments.