How to Automate and Optimize Your Data Pipeline with AI Agents

Vid Bevčar, Lead Python engineer

Introduction

Data pipelines are essential for any data-driven business. However, they can be challenging from maintenance perspective. Manual work, ongoing supervision, and frequent adjustments might be needed to keep up with the changes in data sources, formats, and needs. What if there was a way to automate and optimize your data pipeline with minimal human input?

In this article, we will show you how to use AI agents, which are autonomous software programs that can interact with their environment and other agents to achieve specific goals, to improve your data pipeline automation and flexibility. This blog post will cover what AI agents are, how they work, and how they can help you save time, money, and resources while delivering high-quality and reliable data.

The AI Agent Revolution in Data Management

Our exploration into AI agents for data pipeline management revealed their potential to not only take over routine tasks from data engineers but also to elevate the adaptability of pipelines to new heights. Despite the emergent stage of technologies like Large Language Models (LLMs) and AI agents, their capability to significantly improve operational outcomes was clear, along with some unexpected challenges.

What are AI agents?

AI agents are autonomous implementations of artificial intelligence that can perceive, decide, and act to achieve specific goals. They can leverage large language models, like Mistral, Llama and GPT-4 for natural language and text data (Json, code, …) understanding and reasoning. When coding is required, they can combine the power of LLM’s with code interpreters. They can leverage a multitude of tools to observe, remember and take actions in the outside world, such as Web browsing, DBs, and APIs.

The Problem

Current data pipelines are still quite rigid in a way that every change in input or output data format requires human intervention. Can we leverage the power of AI agents to “auto heal” pipelines when a change in data format is detected?
Desired workflow:

User requests additional column in output dataset
AI agents research available data sources to see where to get the new data from
AI agents update pipeline
AI agents perform test
Pipeline backfills the data

Question: ChatGPT can write a code for a data pipeline. Why would I need agents?
Answer: Yes, but human interaction is required. Someone needs to write a prompt, then take the code, run it, test and deploy.

Tools

To interact with the outside world, AI agents require “tools”. Tools can be implemented as methods with extensive documentation, to allow LLMs to choose the correct one and use it properly. For the PoC implemented these tools:

Problem solving

In an ideal world, this problem solving would look straightforward.

In reality, things can go wrong, for example:

Selected data source does not contain desired field
Extraction code has bugs
This is where AI Agents shine; when faced with a problem, they can adapt the strategy and reason about appropriate solutions.

End results and findings:
AI agents are way smarter than expected. When faced with bugs in the code they composed, they were able to recover. They also did very well on data source selection - when information was not available in the one, they quickly looked in another.
LLMs are great at searching unstructured data. Searching through multiple data sources for correct information took near seconds. And that is without prior knowledge about datasets.
Writing correct prompts took longer than expected. Key takeaway is that you need to be super exact about intentions and tool documentation. Leave no room for interpretation.

Conclusion

This blog post showed the power of AI agents in making data pipelines smarter and more adaptable. We explored their ability to keep up with changing data sources, formats, and needs, and how they work together with other agents and tools to tackle complicated tasks. We also walked through an example of creating a self-updating data pipeline that rises to any data handling challenge. By incorporating AI agents into their workflows, data engineers can harness advanced technology to drive innovation and improve efficiency in their fields. This sets the stage for significant progress in the domain of data management and analysis, shaping the future direction of these disciplines.

Stay tuned for our upcoming blog post, where we'll delve even deeper into the latest advancements using this cutting-edge technology!