Transforming Apache Airflow Monitoring with Gen AI-Powered PoC

Organizations running large-scale data workflows on Apache Airflow often reach a breaking point where traditional monitoring simply cannot keep up. With hundreds of directed acyclic graphs (DAGs) running across environments, teams spend enormous time tracking failures, validating ETL job health, and navigating through Airflow’s UI to understand what’s happening in real time. As the operational load increases, the cost of missed alerts, unnoticed bottlenecks, and slow troubleshooting becomes significant, affecting both productivity and data reliability.

To improve the Airflow experience, Wavicle built a proof of concept (PoC) called Airflow Genie (AFG) a generative AI-powered layer designed to make monitoring and managing ETL pipelines faster, easier, and more reliable. The goal was to enable teams to ask questions in plain language and receive real-time insights to simplify monitoring, speed up issue resolution, and improve Airflow operations.

Reimagining Apache Airflow monitoring

Wavicle’s goal with this PoC wasn’t to replace Airflow’s UI, but to enrich it with a layer of intelligence that Airflow doesn’t natively offer. The team aligned the PoC around the following building blocks:

• Enable natural-language interaction for Directed Acyclic Graph (DAG) execution, monitoring, and querying, making Airflow operations more intuitive.
• Fetch and visualize cluster metrics dynamically to deliver clear, actionable insights.
• Automate job management, failure handling, and status checks to significantly reduce manual effort.
• Integrate smoothly with existing Airflow environments through a scalable, flexible architecture.
• Optimize query generation and execution to improve efficiency and minimize resource usage.
• Enhance accessibility to critical DAG information, including statuses, dependencies, and historical failure patterns.

Building the Airflow Genie solution

Here’s how the Wavicle team approached building AFG and translating the PoC vision into a working solution.

1. Setting up a secure Airflow environment

The team began by setting up a local Airflow environment, replicating the actual DAGs while anonymizing sensitive information. Transformation scripts ensured that testing could be done safely without exposing real production data.

2. Integrating the right AI models

The initial design used API-based LLMs, but for privacy and control, the team moved to local LLMs.

Gwen 2.5 Pro was integrated through Ollama to generate optimized SQL queries.

Llama 3.1 was used for natural conversations, general queries, and troubleshooting support.

This combination created a dual-model setup where one was optimized for accuracy and the other for dialogue.

3. Optimizing performance & storage

To maintain system responsiveness, ChromaDB was replaced with Redis for caching vector embeddings. This shift significantly improved retrieval speed and overall latency during interactions.

4. Building the real-time dashboard

A central part of AFG is its live dashboard, which provides continuous visibility into pipeline health. The dashboard showcases:

• Real-time metrics on DAG execution and performance status
• Recent failures and error trends
• Longest-running DAGs for identifying bottlenecks
• Slot utilization to optimize resource availability
• Overall cluster health for system monitoring
• DAG dependency graph for better context

5. Merging chatbot and dashboard

The final step was integrating the generative AI chatbot directly into the dashboard, allowing users to interact with real-time and historical metrics through natural language within a single interface.

Tech stack overview of Airflow Genie 

High level architecture Airflow Genie POC

Technical Architecture – Apache Airflow Genie POC

Airflow Genie PoC outcomes

The outcome of the PoC demonstrated clear operational and business benefits:

• Improved resolution time: Manual checks that previously took 5–20 minutes dropped to 7–20 seconds through AFG.
• Increased operational efficiency: Teams could instantly view failure details, DAG status, and dependencies, helping them resolve issues faster and avoid unnecessary downtime.
• Enhanced reliability and monitoring: With consistent tracking and conversational insights, undetected failures and bottlenecks reduced significantly.
• Strengthened scalability and security: A local API-driven model architecture provided better data security while still allowing the system to scale and evolve
• Customized visualization: The custom dashboard offered clarity for both technical teams and business stakeholders.

What the Airflow Genie PoC proved

Wavicle’s Airflow Genie PoC illustrates how generative AI can significantly elevate Airflow operations by making them faster, more efficient, and easier to manage. With reduced manual effort, quicker issue resolution, and clearer visibility delivered through conversational interfaces and real-time intelligence, AFG shifts teams from reactive fixes to proactive, insight-driven workflow management. Its secure, scalable architecture powered by local LLMs also lays a strong foundation for future advancements such as deeper automation, voice-enabled interaction, and seamless cloud-native extensions.

If your organization is looking to modernize Airflow monitoring, streamline operational effort, or introduce generative AI into your data ecosystem, Wavicle can help. Reach out to explore how capabilities like AFG can strengthen your Airflow environment.