arXiv:2605.26165v1 Announce Type: cross Abstract: Agentic RAG systems that equip language models with dozens to hundreds of tool definitions face a critical resource conflict: tool schemas consume the same context window needed for retrieval-augmented generation. We present the first systematic study of this tool-context trade-off, evaluating 14 models spanning 1.5B-32B local models plus one frontier API model across 6,566 controlled API calls at three context budgets (8K, 16K, 32K) with 28 tool definitions. Applying TSCG conservative-profile compression (44-50% schema token savings), we obser
The proliferation of complex agentic AI systems necessitates efficient context management to scale their capabilities within current compute constraints.
Efficient tool-schema compression can significantly enhance the operational scope and economic viability of advanced AI agents, pushing their practical deployment forward.
This research provides a concrete method to improve the performance of agentic RAG systems, directly impacting their ability to handle more tools and execute more complex tasks.
- · AI agents developers
- · Cloud AI providers
- · Developers of RAG systems
- · Enterprise adopting AI agents
- · Inefficient AI agent architectures
AI agents become more capable and cost-effective due to better context utilization.
Accelerated adoption of autonomous AI agents across various industries due to improved efficiency.
New classes of AI applications become feasible as context window limitations are significantly mitigated for agentic systems.
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