📅 2025-04-16 — Session: Explored DAGs vs Flex Flows and Hybrid Systems
🕒 17:45–18:30
🏷️ Labels: Dags, Flex Flows, Hybrid Systems, Pythonblocks, Developer Experience
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to evaluate different architectural designs in AI workflow automation, specifically focusing on Directed Acyclic Graphs (DAGs), Flex Flows, and hybrid systems involving Prompt and Python blocks.
Key Activities
- Evaluated the differences between DAGs and Flex Flows, focusing on aspects like expressiveness, execution control, cognitive load, reusability, and development effort.
- Explored the limitations of Retrieval-Augmented Generation (RAG) and tool-using agents within DAG frameworks.
- Outlined a conceptual framework for integrating prompt and Python blocks into a unified system.
- Discussed the Tiny Engine Loop mindset for building modular and composable software.
- Provided guidelines for defining a PythonBlock execution contract.
- Developed strategies for enhancing developer experience in PythonBlocks.
- Emphasized the need for empathetic debugging tools in Python development.
- Summarized the architecture of a modular OpenAI tool adapter for PromptFlow.
Achievements
- Gained insights into when to use DAGs versus Flex Flows and potential enhancements for DAGs.
- Conceptualized a hybrid system for seamless data flow between prompt and Python blocks.
- Established best practices for PythonBlock execution contracts and empathetic debugging tools.
Pending Tasks
- Implement the suggested enhancements for DAGs to incorporate looping and conditional logic.
- Develop the ‘FriendlyErrorCatcher’ tool for empathetic debugging.
Labels
DAGs, Flex Flows, Hybrid Systems, PythonBlocks, Developer Experience