Hello everyone,
I would like to share a conceptual architecture I have been developing for AI application reliability.
Current LLM systems are highly capable in understanding information and generating responses, but long-running and complex tasks still face challenges such as:
- Loss of original task objectives
- Context overload
- Unclear execution boundaries
- Lack of validation mechanisms
- Difficulty maintaining responsibility boundaries
Task State System proposes an application control layer between user requirements and AI capabilities.
The core idea is:
AI Capability Layer
+
Task State Control Layer
+
Tools / Data / Execution Environment
The architecture introduces:
- Task Objective
- Task State
- Task Boundary
- Execution Conditions
- Validation and Correction
The goal is not to improve the intelligence of AI models themselves, but to explore how AI capabilities can be managed as reliable task execution systems.
This project is currently a conceptual architecture and documentation effort.
The core overview and architecture documentation of this project are available in English.
The repository also contains additional documentation in Chinese for detailed design discussions.
I would like to receive feedback from developers and researchers regarding:
- How current AI agent systems approach task continuity and state management
- Existing approaches for application-level AI control
- Validation and responsibility mechanisms in long-running AI workflows
Thank you for any feedback or discussion.