DQDF: Data-Quality-Aware Dataframes
Phanwadee Sinthong, Dhaval Patel, et al.
VLDB 2022
Although LLMs can generate tools for generic domains and tasks, they struggle with enterprise-related domains that in- volve proprietary APIs and data schemas. We present Tool- Smith, a framework for autonomously generating and validat- ing agent-compatible tools. Given an API specification and a Tool Specification Requirement (TSR), ToolSmith produces a tool function and verifies it through a closed-loop process: it creates natural language (NL) tests and executes the tool in a secure agent sandbox for validation. For state-changing tools, ToolSmith confirms outcomes by querying the API with pa- rameters derived from the NL tests. If the tool fails to produce the desired output, ToolSmith generates diagnostic feedback to iteratively regenerate it. By ensuring both functional cor- rectness and agent compatibility, ToolSmith enables reliable automation of enterprise workflows.
Phanwadee Sinthong, Dhaval Patel, et al.
VLDB 2022
Tyler Baldwin, Wyatt Clarke, et al.
Big Data 2022
Amit Alfassy, Assaf Arbelle, et al.
NeurIPS 2022
Avirup Saha, Prerna Agarwal, et al.
CODS-COMAD 2024