arXiv:2605.30862v1 Announce Type: cross Abstract: Text2SQL agents powered by LLMs translate natural language intent into SQL by exploring the data system through tool calls before formulating the query. However, to ensure secure and scoped access, data systems construct environments with explicit API surfaces. We study and categorize these APIs exposed today as either coarse-grained or fine-grained and posit that choosing between them presents a fundamental tradeoff between cost-efficient exploration and accurate SQL generation. Most data systems expose fine-grained APIs, but this inadvertentl
The proliferation of LLMs and their application in agentic systems is driving research into their practical limitations and inefficiencies, particularly when interacting with complex data environments.
This research highlights a fundamental trade-off in the design of API surfaces for data systems interacting with AI agents, directly impacting efficiency, cost, and security of advanced AI applications.
The understanding of how to optimally design data system APIs for agentic exploration will evolve, leading to more efficient and secure Text2SQL solutions and broader agent-data interactions.
- · AI agent developers
- · Data system providers
- · Enterprises adopting AI agents
- · Inefficient Text2SQL solutions
- · Data systems with poorly designed APIs
Improved performance and reduced cost for Text2SQL agents by optimizing API interactions.
Accelerated adoption of AI agents in data-intensive tasks across various industries due to enhanced reliability and efficiency.
The emergence of new standards or best practices for API design specifically tailored for AI agent interaction, influencing future software architecture.
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Read at arXiv cs.AI