AI Agent Workflow Planning for Complex Task Execution
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Solution Overview
Problem
Existing online systems face challenges in efficiently managing complex and ambiguous tasks, requiring significant coordination and resources due to error-prone processes in retrieving and applying relevant components, which are time-consuming for developers.
Innovation Solution
An online system employs a generative machine-learned model powered by an AI agent that breaks down tasks into manageable subtasks, using a prompt to extract a sequence of actions from a model serving system, and executes these actions to complete portions of the task, leveraging a transformer-based architecture and large language models for efficient task execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual coordination methods are used to retrieve and apply components for complex tasks, then developers can complete tasks with existing tools, but the process becomes error-prone and time-consuming
Solution Approach 1:
An AI agent is introduced as an intermediary between the task description and the component retrieval/application process. The AI agent automatically parses task descriptions, identifies required components, and coordinates their application, eliminating manual coordination errors while reducing task completion time through automation.
Solution Approach 2:
The system enables self-service by allowing the AI agent to autonomously retrieve and apply components based on task descriptions without requiring developer intervention for coordination. The agent independently manages the entire component lifecycle from identification to application, reducing both error rates and time consumption.
2Productivity
If significant coordination resources are allocated to manage complex tasks, then task completion can be achieved, but resource consumption increases
Solution Approach 1:
The patent replaces manual mechanical coordination processes with an AI-based automated system. The AI agent uses natural language processing and machine learning to automatically manage component retrieval and application, significantly improving productivity while reducing the human resources required for task coordination.
3Ease of operation
If developers manually retrieve and apply components for complex tasks, then tasks can be completed, but the process requires significant developer time and effort
Solution Approach 1:
The AI agent enables self-service by automatically handling component retrieval and application based on task descriptions. Developers simply need to provide high-level task descriptions, and the agent independently manages all coordination details, dramatically reducing both developer effort and time required for task completion.
Data Source
AI summary
An online system receives a description of a task and generates a prompt for a generative machine-learned model. The prompt includes the description of the task and a request for a sequence of actions associated with the task, and a list of candidate actions and attributes related to the list of candidate actions. The online system provides the prompt to a model serving system deployed with the machine-learning model for execution. The online system obtains a response from the model serving system to extract wherein at least one workflow including the sequence of actions once executed completes a portion of the task. The online system receives, as output from the machine-learned model, the sequence of actions associated with the description of the task. The sequence of actions is executed in order to complete at least a portion of the task.