Less Context, Better Agents: Efficient Context Engineering for Long-Horizon Tool-Using LLM Agents
📰 ArXiv cs.AI
Learn how to efficiently engineer context for long-horizon tool-using LLM agents to improve their performance and reduce inference cost
Action Steps
- Evaluate the impact of context overflow on LLM agent performance using tools like Model Context Protocol
- Configure GPT-5 models with different context engineering strategies to optimize performance
- Test the agents on a benchmark task, such as automated expense itemization, to compare results
- Apply efficient context engineering techniques to reduce stale-state errors and inference cost
- Analyze the results to identify the most effective context engineering approach for long-horizon tool-using LLM agents
Who Needs to Know This
Researchers and developers working on LLM agents for enterprise workflows can benefit from this knowledge to improve the efficiency and accuracy of their agents
Key Insight
💡 Less context can lead to better agents, as excessive context can cause errors and increase inference cost
Share This
💡 Efficient context engineering can improve LLM agent performance and reduce inference cost! #LLM #AI
Key Takeaways
Learn how to efficiently engineer context for long-horizon tool-using LLM agents to improve their performance and reduce inference cost
Full Article
Title: Less Context, Better Agents: Efficient Context Engineering for Long-Horizon Tool-Using LLM Agents
Abstract:
arXiv:2606.10209v1 Announce Type: new 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, fu
Abstract:
arXiv:2606.10209v1 Announce Type: new 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, fu
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