AI Workflow Sketching for Faster Intent-Accurate Automation
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Solution Overview
Problem
Existing AI-assisted workflow generation technologies suffer from significant latency and inefficiency due to lengthy generation times, lack of transparency, and the need for repetitive user input, leading to increased processing and token usage, while manual workflows require tedious step-by-step setup without discerning user intent.
Innovation Solution
A system utilizing an AI agent that generates and updates workflow outlines through a conversational interface, allowing users to iteratively refine sketches of workflows, confirming intent before full generation, thereby reducing latency and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If AI generates complete workflow code directly from user prompts, then automation capability is achieved, but generation time and latency increase significantly
Solution Approach 1:
The patent segments the workflow generation process into two distinct phases: (1) generating a high-level workflow outline/sketch that captures the overall structure and logic, and (2) subsequently generating detailed executable code for each workflow step. This segmentation allows the system to provide immediate value through the outline while deferring computationally intensive code generation, thereby reducing perceived latency and enabling faster iteration.
Solution Approach 2:
The system performs preliminary action by generating a workflow outline/sketch before generating the complete executable code. This preliminary workflow structure serves as a validated blueprint that confirms user intent before committing to full code generation, reducing the need for rework and enabling faster overall development cycles.
2Extent of automation
If AI generates complete workflow code in one step, then automation is achieved, but token usage and processing resources increase
Solution Approach 1:
The generation process is divided into resource-light outline generation followed by resource-intensive code generation only for approved workflows. This segmentation ensures that processing resources and tokens are consumed only when necessary, after user validation of the workflow logic, thereby optimizing resource efficiency.
Solution Approach 2:
The system performs partial action by generating only the workflow outline initially rather than the complete code. This partial generation uses fewer tokens and processing resources, and the remaining code generation is triggered only when the user approves the outline, avoiding wasteful resource consumption on unwanted workflows.
3Productivity
If AI generates workflow code without intermediate confirmation, then speed is prioritized, but accuracy of user intent decreases
Solution Approach 1:
The system implements feedback by presenting the generated workflow outline to the user for review and confirmation before proceeding to code generation. This feedback loop ensures that the AI correctly understands user intent, allowing users to correct misunderstandings early, thereby improving intent accuracy without significantly impacting overall productivity.
Solution Approach 2:
The workflow outline serves as a preliminary validation step that confirms user intent before full code generation. This preliminary action ensures accuracy by allowing user review and correction, while the system maintains productivity by enabling quick iterations on the outline itself before committing to detailed code generation.
4Manufacturing precision
If users manually build workflows step by step, then precision is achieved, but time consumption increases
Solution Approach 1:
The AI performs preliminary action by automatically generating a workflow outline based on user prompts, capturing the overall structure and logic. This preliminary draft significantly reduces the time required compared to manual step-by-step construction, while users retain the ability to review and refine the outline for precision, achieving both speed and accuracy.
Solution Approach 2:
The system segments the workflow creation process into AI-generated outline (fast, captures overall structure) and user-refined details (precise, ensures accuracy). This segmentation allows users to benefit from AI speed for the bulk of workflow construction while maintaining manual control for precision-critical adjustments, optimizing both time and precision.
Data Source
AI summary
A system may receive a user prompt in a conversational interface and may determine an intent of a user using one or more artificial intelligence (“AI”) models. The system may generate a recipe sketch including a set of rough automated operations configured to accomplish a task, and it may provide a graphical user interface displaying the recipe sketch to the user. The system may receive a user input modifying the recipe sketch and, using the one or more AI models, modify the recipe sketch, and it may display the modified recipe sketch in the graphical user interface. The system may use the modified recipe sketch with the one or more AI models to generate a full, solved recipe, such as a set of executable operations to accomplish the task. In some cases, the graphical user interface may be updated to illustrate the generated set of executable operations.