This project is a collection of architectural templates and design patterns for building autonomous AI agents. It provides a framework for transitioning from simple prompt-response loops to goal-oriented systems that utilize structural patterns to increase autonomy and improve the reliability of complex task completion.
The framework focuses on reasoning orchestration, specifically through the implementation of reflection and self-correction cycles. It enables the coordination of specialized agents via task delegation and state sharing to solve complex problems.
The architectural surface covers autonomous workflow automation, including plan-and-execute workflows, tool-use execution loops, and iterative prompt refinement. It also includes mechanisms for dynamic context windowing to optimize model attention.
The project is implemented as a series of Jupyter Notebooks.
