Language Model Automation Scheduling for Delayed Actions
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Solution Overview
Problem
Generative response engines lack a concept of time, making it difficult to perform automated actions at specific future times, and often face issues with back-pressure and rate-limiting when handling multiple concurrent requests.
Innovation Solution
Integrating an automations engine that recognizes temporal components in user prompts, translates them into scheduling instructions, and interacts with a scheduler to perform delayed actions, while managing back-pressure through time spreading and fine-tuning to handle ambiguous requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generative response engines process multiple concurrent requests, then productivity increases, but back-pressure and rate-limiting issues worsen
Solution Approach 1:
The system performs preliminary actions by translating user prompts into scheduled automation tasks before executing them. The automations engine pre-processes requests by identifying temporal components and creating scheduled actions, which are then executed at designated times. This preliminary translation and scheduling mechanism distributes the processing load over time, preventing back-pressure and rate-limiting issues while maintaining high productivity.
2Speed
If generative response engines execute tasks immediately, then response speed increases, but ability to perform delayed actions decreases
Solution Approach 1:
The system implements dynamics by making the execution timing of tasks flexible and adjustable. The automations engine dynamically determines when to execute tasks based on temporal components in user prompts, allowing immediate execution when needed and delayed execution when specified. The scheduler dynamically manages task execution timing, enabling the system to adapt between fast response and scheduled execution based on user requirements.
3Reliability
If the system spreads tasks over time, then back-pressure is reduced, but task execution delay increases
Solution Approach 1:
The system implements feedback mechanisms where the automations engine continuously monitors scheduled tasks and their execution status. The scheduler receives feedback about task completion and user interactions, adjusting the timing and execution of subsequent tasks accordingly. This feedback loop ensures that tasks are spread over time to reduce back-pressure while minimizing unnecessary delays by executing tasks as soon as resources are available and user interaction is detected.
Data Source
AI summary
The present technology pertains to a system for generating automations based on a request/prompt from a user to a language model. The language model receives a prompt in a conversation thread with a user. When the prompt includes a request for a delayed action (i.e., an automation), the request is forwarded to an automations engine, which determines the delayed action from an action component of the request and a time at which to perform the delayed action from a temporal component of the request. A scheduling instruction based on the determined time is sent to a scheduler, which signals when the time occurs, triggering the automations engine to perform the delayed action, for example, by acting as the user's delegate and sending a delayed prompt to the language model to elicit a response and amending part or all of the resulting conversation to the conversation thread with the user.