In today’s data-driven world, effective workflow management and AI are crucial for success. However, there’s a notable gap between Airflow and AI. Our presentation offers a solution to close this gap. Proposing MCP (Model Context Protocol) server to act as a bridge. We’ll dive into two paths: AI-Augmented Airflow: Enhancing Airflow with AI to improve error handling, automate DAG generation, proactively detect issues, and optimize resource use. Airflow-Powered AI: Utilizing Airflow’s reliability to empower LLMs in executing complex tasks, orchestrating AI agents, and supporting decision-making with real-time data. Key takeaways: Understanding how to integrate AI insights directly into your workflow orchestration. Learning how MCP empowers AI with robust orchestration capabilities, offering full logging, monitoring, and auditability. Gaining insights into how to transform LLMS from a reactive responder to a proactive, intelligent, and reliable executor. Inviting you to explore how MCP can help workflow management, making AI-driven decisions more reliable and turning workflow systems into intelligent, autonomous agents.