My engineers typically spend 30% of their time debugging data. On some days, that number climbs to 50% or more.
If you’re a data or analytics engineer, you know the familiar dread of a broken metric. A stakeholder reports an anomaly, and the investigation begins. The typical path involves manually navigating a sprawling codebase to locate exactly where the metric is defined. You run warehouse queries to measure the scope of the anomaly, painstakingly walking upstream models to find exactly where the data diverges. Finally, you synthesize a root-cause narrative to share with the team.
If you’re lucky, you wrap the task up in minutes. If you’re not, the investigation drags into the next day. By the time you’re done, the team has already missed a high-stakes deliverable.
This manual process is a massive pain point. It requires holding complex logic in your head while constantly context-switching between code repositories, SQL IDEs, and ticketing systems. Make the wrong assumption or miss a tiny detail, and you might have to start all over again.
Ultimately, this tedious process follows a strict logical loop: Identify the erred metric → Establish a hypothesis → Explore the transformation logic → Query the actual database state to confirm the hypothesis → Repeat until resolution → Propose next steps.
I decided to build an AI agent to automate this workflow. To achieve this, I needed an engine capable of handling non-linear, multi-step reasoning. LangGraph is a runtime orchestration framework specifically designed for building stateful, multi-agent systems.
Unlike standard linear AI pipelines, LangGraph allows agents to loop, maintain shared state, and gracefully recover from failures. By delegating tasks to specialists with narrow scopes and custom tools, the framework drastically reduces hallucinations and makes the entire workflow traceable, node by node.
This multi-agent system relies on a crew of specialist agents, each with a highly focused job:
An AI agent is only as intelligent as the environment it operates in. To ensure a debugging agent works efficiently, data teams must invest in foundational engineering practices:
Now, when an engineer spots a failed model or test, they simply click a button. This kicks off a conversation with the agent using a pre-generated prompt that includes the specific project, model, and symptom. The agent autonomously runs the investigation, returning detailed logs and reasoning, and stops when it identifies the root cause.
The engineer can then evaluate the diagnosis with a separate agent. If they are satisfied with the result, the agent will automatically create an issue ticket containing the detailed report and proposed next steps in the body.
Tasks that used to take hours now run concurrently in the background, allowing the engineer to devote their time to building better infrastructure, cleaning up legacy code, and writing documentation.
In my next post, I’ll write about how to deploy this system so engineers & analysts across teams can chat with it.
#AI #agent #dataengineering #analyticsengineering #langgraph
From Hours to Minutes: Automating Data Debug with Multi-agent Systems was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.