Learn how to transform the ReAct loop from interactive agent logic into production-ready, automated workflows using DAG-based orchestration. This hands-on module demonstrates how to structure an agent's decision-making process as concrete workflow tasks using Prefect in Google Colab, teaching fundamental concepts that apply to enterprise tools like Apache Airflow.
| Lesson | Est. Delivery Time | Skills |
|---|---|---|
| Concepts | 10 min | Understand ReAct to DAG mapping and orchestration concepts |
| Prefect Environment Setup | 15 min | Install Prefect and configure Colab environment |
| Building Agent DAGs | 35 min | Create automated agent workflows with conditional logic |
| Total content | 60 min |
- Basic Python programming experience
- Understanding of AI agents and ReAct loops (Module 4.1)
- Experience with MCP integration (Module 4.5)
- Google account for Colab access
This is a guided code walkthrough designed for Google Colab. Students will run code cells step-by-step to build automated agent workflows using:
- Prefect for workflow orchestration
- Task decorators for defining agent reasoning steps
- Conditional branching for complex agent decision logic
- DAG visualization for understanding agent workflows
By the end of this module, students will be able to:
- Transform abstract ReAct loops into concrete workflow tasks
- Design DAGs that mirror agent reasoning processes
- Implement conditional branching based on agent analysis
- Create workflow visualizations for agent decision flows
- Apply orchestration concepts to production agent systems
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