Less Context, Better Agents: Efficient Context Engineering for Long-Horizon Tool-Using LLM Agents
arXiv:2606.10209v1 Announce Type: cross Abstract: Large language models 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 Model Context Protocol tools. We evaluate four GPT-5 configurations on a 50-task hotel expense benchmark: no user model, full conversation history, context pruned to the last 5 tool call/response pairs, and pruning with a
The proliferation of LLMs in enterprise settings is exposing practical limitations like context management, making solutions for efficiency and reliability critical for adoption.
This research directly addresses core technical challenges that limit the scalability and robustness of LLM agents, impacting their viability for complex enterprise automation.
Optimized context engineering techniques can significantly reduce operational costs and improve the performance of long-horizon LLM agents in real-world business applications.
- · AI software vendors
- · Enterprises adopting AI agents
- · Cloud computing providers
- · Inefficient LLM architectures
- · Manual workflow processes
Enterprise AI agents become more reliable and cost-effective for automating complex tasks.
Increased adoption of AI agents drives demand for more sophisticated and efficient LLM deployments.
The collapse of certain white-collar workflows accelerates as dependable AI agents handle a wider range of business operations.
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Read at arXiv cs.LG