arXiv:2606.08300v1 Announce Type: new Abstract: Many real-world queries over personal data span multiple applications and require structured planning, as individual tools expose only partial information. While LLMs show strong reasoning and tool use, reliably executing multi-step, cross-tool queries remains challenging. We introduce a system that converts natural language queries into structured graphs and executes them via a deterministic planner. Our approach uses depth-first search to resolve dependencies and combine results across tools, improving reliability and enabling queries beyond tr
The rapid advancement in large language models has exposed the challenge of reliable multi-step, cross-tool execution, making this a timely development for practical AI application.
This development addresses a critical bottleneck in deploying autonomous AI agents that can reliably interact with complex real-world systems, moving beyond simple single-tool use to sophisticated multi-application queries.
AI systems can now more reliably plan and execute complex queries spanning multiple applications by converting natural language into structured graphs, improving both autonomy and dependability.
- · AI software developers
- · Enterprise AI users
- · Automation platforms
- · Manual data integration workflows
- · Legacy enterprise software with poor API integration
Increased reliability and capability for AI agents to perform complex tasks across diverse digital environments.
Accelerated adoption of AI agents in white-collar workflows, leading to significant productivity shifts and potential job redefinitions.
The development of highly complex, interconnected AI ecosystems that autonomously manage and optimize enterprise operations.
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Read at arXiv cs.LG