Natural Language Workflow Execution via LLM-Generated DAGs
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Solution Overview
Problem
Existing natural language-based command processing systems face challenges in reducing data errors during data update execution, as errors can occur in natural language processing or command conversion.
Innovation Solution
The system converts natural language descriptions of workflows into executable programs using Large Language Models (LLMs) to segment the language into specific steps, which are then converted into parameterized calls to agents from an agent library, with dependencies tracked to create a conditional control flow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language commands are converted into application specific commands, then user interaction ease is improved, but data errors may occur in processing or conversion
Solution Approach 1:
The patent introduces an intermediary verification mechanism between natural language input and command execution. A validation system checks the converted commands against the original natural language statement, acting as a mediator to detect and prevent errors before execution, thus maintaining both ease of operation and data accuracy
Solution Approach 2:
The system implements feedback loops where the converted commands are validated against the original natural language input. Error detection mechanisms provide feedback to users about potential misinterpretations, allowing correction before execution, thereby resolving the contradiction between ease of use and reliability
2Ease of operation
If natural language processing is used to convert commands, then ease of operation is improved, but errors in processing may occur
Solution Approach 1:
The patent applies preliminary validation actions before command execution. The system pre-processes and validates the converted commands against the original natural language input, identifying potential errors in advance. This preliminary check ensures accurate conversion while maintaining simple natural language input
Solution Approach 2:
The patent replaces traditional rigid command parsing mechanisms with AI-based natural language processing that incorporates validation. This substitution allows for more accurate interpretation of natural language while maintaining simplicity, resolving the contradiction between input ease and conversion precision
Data Source
AI summary
Systems and techniques for are described herein. Natural language workflow descriptions are received from expert users, the descriptions are processed using language models to identify action sequences, and directed acyclic graphs (DAGs) are generated that represent workflows by mapping actions to automated agents. These DAGs are stored as composite action recipes in a vector database. End users can then submit natural language queries, which the system matches to stored recipes. Selected recipes are executed in a sandboxed environment, with the system sequentially invoking automated agents according to the DAG structure. The systems and techniques described herein provide real-time feedback during execution and support features such as graphical workflow validation, dynamic parameter collection, role-based access control, and dry run simulations. This approach enables efficient creation and reuse of complex workflows based on natural language inputs.