Personal AGI — Pushing GPT-4-turbo beyond all limits

🌈 Abstract

The article discusses the concept of "Personal AGI" - a system that aims to accompany and assist the author in various aspects of their daily life. It explores the key components of such an agent AI system, including memory, logic, tools, and interface. The article also delves into the challenges and limitations of building such a system, such as maintaining long-term memory, processing complex content, and optimizing performance and cost-effectiveness.

🙋 Q&A

[01] Scope of the "Personal AGI" system

1. What are the key components of the "Personal AGI" system described in the article?

• Memory: Short-term memory for the current conversation, long-term memory for storing information across conversations, and "context" or "document" for larger sets of information.
• Logic: Combination of programming logic and advanced prompts for self-reflection, intent recognition, planning, and content generation.
• Tools: Ability to select and use various tools, both external services and the agent's own capabilities, to solve problems.
• Interface: Text chat or voice messages, as well as API integration for automatic interactions.

2. How does the agent handle environmental information that is not directly accessible through tools? The agent can access certain information by default, without needing any tools, such as the current date and time, location, weather, device status, and other relevant data. This information is used to adjust the agent's behavior not only during conversations but also when performing tasks independently.

[02] Short-term memory, long-term memory, and document handling

1. How does the agent manage short-term and long-term memory?

• Short-term memory: Includes the content of the current conversation and additional information read during the generation of responses. This information is only available for the duration of the conversation.
• Long-term memory: A more complex challenge without a current solution. The agent's memory is a database searched during conversations to load necessary context for responses, with various areas for storing information about people, resources, locations, and other categories.

2. How does the agent handle longer content, such as blog articles? The agent divides longer content into smaller fragments and processes each one separately. The LLM cannot generate a full response containing the entire content, so the agent uses an identifier to represent the content, which is then programmatically replaced with the proper content.

[03] Tool organization and handling

1. What are the key considerations in designing tools for the agent?

• Consistent input/output interface
• Intermediate layer for protection against model hallucinations and limiting access to selected actions
• Ability to perform multiple actions within a single query
• Proper data transfer between tools, including the use of identifiers or links to files
• Providing additional data needed to complete a task
• Robust error handling and reporting

2. How are the tools organized in the agent's system? The tools are divided into categories, such as media, business, communication, data handling, memory, action planning and alerts, content management, and additional tools. This organization allows the agent to effectively utilize the various capabilities and services available to it.

[04] Agent Logic

1. What are the key steps in the agent's logic for processing a message?

1. Gathering context: Retrieving environmental data, summarizing the ongoing conversation, listing skills, and accessing memory areas.
2. Self-reflection: Considering the received message and comparing it to available memory, environmental data, and skills to determine the appropriate response.
3. Action plan: Determining the necessary queries to its own memory and the actions to be taken.
4. Recalling: Generating a list of questions to different memory areas to find relevant information.
5. Internet search: Performing additional searches on the internet to support the response, if required.
6. Taking actions: Executing the necessary tools and actions based on the action plan.

2. What are some of the challenges and areas for improvement in the agent's logic?

• Organizing memory in a more efficient graph structure or finding alternative ways to link records in different memory areas
• Designing mechanics for dynamically building or scaling the memory structure
• Modifying the logic to allow each action to access different memory areas or search results
• Breaking down larger prompts into smaller, parallel steps
• Improving self-consistency in prompt combinations
• Enhancing error handling and overall performance optimization
• Adding monitoring and analysis capabilities for easier debugging and optimization

[05] Conclusions, Observations, and the Great Opportunity

1. What are the key limitations and challenges in building a truly universal autonomous agent system?

• Maintaining attention on the available context, even with advanced models like GPT-4-turbo
• Complexity in evaluating logic consisting of multiple prompts
• Slow speed of model operation, especially when processing longer documents or involving multiple prompts
• High costs of operating the models, especially in situations requiring a series of prompts
• Importance of creating and verifying prompts, which remains a critical role for now

2. What is the current state of "Personal AGI" and the opportunities it presents?

• Creating a system capable of addressing even a few daily life activities has become within the reach of many programmers
• Developing a truly universal system that can be widely available to users remains beyond current technological capabilities
• This is an opportune time to develop skills and create tools that can support individuals in their daily lives, even if a fully autonomous and universal agent system is not yet feasible.