📅 2025-04-16 — Session: Explored DAGs vs Flex Flows and Hybrid Systems
🕒 17:45–18:30
🏷️ Labels: Dags, Flex Flows, Python, Ai Workflow, Developer Experience
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to evaluate architectural design choices between Directed Acyclic Graphs (DAGs) and Flex Flows, understand the role of RAG and tool-using agents in DAG structures, and explore the design of hybrid systems integrating prompt and Python blocks.
Key Activities
- Evaluated DAGs vs Flex Flows: Discussed their expressiveness, execution control, cognitive load, reusability, and development effort.
- Explored RAG and Tool-Using Agents: Reflected on the limitations and dynamic nature of RAG and tool-using agents within DAG frameworks.
- Designed Hybrid Systems: Outlined a conceptual framework for integrating prompt and Python blocks, focusing on seamless data flow and shared interfaces.
- Tiny Engine Loop Mindset: Introduced a minimalist approach to modular and composable software using a simple execution loop.
- Guidelines for PythonBlock Protocol: Defined best practices for PythonBlock execution contracts.
- Developer Experience: Planned strategies for enhancing developer tools with validation and feedback.
- Empathetic Debugging: Suggested creating ‘FriendlyErrorCatcher’ for better debugging experiences.
- OpenAI Tool Provider in PromptFlow: Reviewed the architecture and features of a modular tool adapter for OpenAI integration.
Achievements
- Clarified when to use DAGs vs Flex Flows and proposed enhancements for DAGs.
- Developed a framework for hybrid systems with prompt and Python blocks.
- Established guidelines for PythonBlock protocols and empathetic debugging tools.
Pending Tasks
- Implement the proposed enhancements for DAGs to incorporate looping and conditional logic.
- Develop and test the ‘FriendlyErrorCatcher’ tool for Python debugging.
- Further explore the integration of OpenAI tools within PromptFlow.