Language Model Actions Engine for Multistep Task Execution
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Solution Overview
Problem
Current language models struggle to perform complex actions and multistep tasks efficiently, requiring extensive manual programming and API endpoints, which limits their versatility and user accessibility.
Innovation Solution
The development of an actions engine within a SaaS network architecture that trains and utilizes language models to perform actions and action chains, enabling them to access and operate software components, thereby reducing manual programming tasks and enhancing user interaction through a natural language interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If language models are trained to perform complex actions and multistep tasks, then their functionality and versatility are improved, but the training complexity and computational resources required increase significantly
Solution Approach 1:
The patent segments the training process into multiple distinct phases: pre-training on general language data, then fine-tuning on specific action datasets, and finally training on multistep task chains. This segmentation allows the model to build capabilities incrementally rather than requiring all complexity to be learned simultaneously, reducing overall training complexity while maintaining versatility.
Solution Approach 2:
The patent applies preliminary action by pre-training the language model on extensive general language data before exposing it to action-specific tasks. This preliminary training establishes a strong foundational understanding of language and context, which simplifies subsequent training on complex actions and multistep tasks, as the model already possesses basic language comprehension skills.
2Adaptability or versatility
If language models execute multiple actions simultaneously across platforms, then user experience and software interoperability are improved, but the system complexity and coordination requirements increase
Solution Approach 1:
The patent introduces an intermediary action engine that sits between the language model and multiple software platforms. This engine translates natural language instructions into platform-specific actions, managing the complexity of cross-platform coordination centrally rather than requiring complex point-to-point integrations between platforms, thereby improving interoperability while containing system complexity.
Solution Approach 2:
The patent creates a universal action execution framework that can handle multiple types of actions across different platforms through a common interface. This multi-functional system allows the language model to interact with diverse software components using the same natural language paradigm, improving software interoperability without requiring platform-specific customization for each interaction type.
3Ease of operation
If manual programming and API endpoints are reduced, then ease of operation and user accessibility are improved, but the reliability and precision of action execution may deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where the language model receives confirmation and correction signals from executed actions. The system monitors whether actions were successfully performed and uses this feedback to refine future action selections and interpretations, thereby maintaining high reliability in action execution even as manual programming requirements decrease and user accessibility increases.
Data Source
AI summary
The subject technology uses an agent architecture for language models and large language models (LMs) to complete a variety of different tasks within software platforms. The agent LMs are trained to determine different action chains that may be used to generate responses to tasks requested by users. The action chains may include a sequence of multiple actions that each complete a portion of the requested task. The agent LMs may be trained to perform different types of action chains using training prompts that teach the agent LMs to use tools that enable the LMs to interact with different software resources. The agent architecture may coordinate multiple agent LMs to complete tasks that require multiple action chains to complete.