Layered-Memory AI Agents for Preference-Aligned Task Execution
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Solution Overview
Problem
Conventional large language models (LLMs) face challenges in performing complex tasks due to unpredictable output, lack of self-awareness, misalignment with diverse human preferences, and inefficiencies in prompt engineering, leading to safety and reliability issues in autonomous agents.
Innovation Solution
Integrating a structured, layered memory system with adaptive machine learning processes and observer agents to dynamically configure agents, using micro-prompts and Bayesian inferencing to align with user-specific preferences and optimize resource use, while reducing AI hallucinations and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional large language models are used to perform complex tasks, then the agents can execute user-level tasks without direct human instruction, but the output becomes unpredictable and misaligned with diverse human preferences
Solution Approach 1:
The patent implements a feedback mechanism where the automated agent receives feedback from human users about their preferences and adjusts its behavior accordingly. This allows the system to learn from interactions and improve alignment with human preferences over time, resolving the contradiction between autonomous execution and reliable predictable output.
Solution Approach 2:
The system dynamically adjusts parameters such as temperature, top-p sampling, and other generation controls based on task requirements and learned user preferences. This enables the agent to maintain reliable and predictable output while still performing complex autonomous tasks by optimizing parameters for each specific context.
2Ease of operation
If conventional large language models are used, then agents can interact with human users, but they lack self-awareness and alignment with user-specific preferences
Solution Approach 1:
The patent implements self-service mechanisms where the automated agent autonomously learns and adapts to user preferences without requiring direct reprogramming or manual configuration. The agent maintains a representation of user preferences and uses this to automatically adjust its responses and behaviors, enabling both easy user interaction and adaptability to individual users.
Solution Approach 2:
The system performs preliminary actions by pre-learning user preferences during initial interactions and storing this information for future use. This allows the agent to be better aligned with user preferences before actual task execution, improving both ease of operation and adaptability.
3Productivity
If prompt engineering is used to improve task performance, then agents can execute specific tasks, but efficiency decreases due to the complexity and time required for prompt construction
Solution Approach 1:
The patent implements self-service prompt generation where the automated agent autonomously constructs and optimizes its own prompts based on the task at hand and learned patterns from previous interactions. This eliminates the need for manual prompt engineering while maintaining high task execution capability, thereby improving productivity without the time loss associated with prompt construction.
Solution Approach 2:
The system performs preliminary actions by pre-processing and structuring information into optimized prompt formats before task execution. This preliminary organization of information enables the agent to execute tasks efficiently without requiring time-consuming prompt engineering during actual operation.
4Extent of automation
If conventional LLMs are used for autonomous agents, then tasks can be automated, but safety and reliability issues arise due to AI hallucinations and unpredictable behavior
Solution Approach 1:
The patent implements feedback mechanisms where the automated agent receives feedback about the accuracy and reliability of its outputs, allowing it to correct hallucinations and improve safety. This continuous feedback loop enables the system to maintain high automation levels while reducing harmful factors through iterative improvement based on real-world performance.
Data Source
AI summary
An example may determine an entity identity associated with an entity. An example may use the entity identity to create an automated agent including a multi-layer memory and a workflow. An example may store context data in a first layer of the multi-layer memory. The context data may be obtained using the entity identity. An example may store at least one machine-learned entity preference in a second layer of the multi-layer memory. The at least one machine-learned entity preference may be machine-learned using the context data. An example may use the at least one second layer of the multi-layer memory including the at least one machine-learned preference to configure or control execution of the workflow by the automated agent.