August 15, 2025
5 min read
@kdnuggets
How I Use AI Agents to Automate Experimentation in Data Science
As data scientists, we wear many hats, often juggling multiple roles in a single day. From building data pipelines withSQL and Python to analyzing data statistically, communicating with stakeholders, monitoring product performance, and running experiments — the workload is intense and varied.
Being a data scientist is exciting because it offers exposure to diverse business aspects and the ability to see the impact of products on users. However, the downside is the constant pressure to keep up. When a product launch underperforms, you need to diagnose the issue immediately. Simultaneously, stakeholders might request quick experiments and clear, balanced explanations that are neither too technical nor too vague.
By the end of the day, it often feels like running a marathon — only to repeat it the next day. This is why I embrace opportunities to automate parts of my job using AI.
Recently, I have started incorporating AI agents into my data science workflows, which has made me more efficient and faster at answering business questions with data.
In this article, I will share:
- How I traditionally perform data science workflows without AI
- How I automate these workflows using AI agents
- The tools I use and the time saved But first, let’s clarify what AI agents are and why they are generating so much excitement.
- Return on investment and resource allocation
- Long-term impact on the platform
- User sentiment and engagement A/B testing is the standard method to assess new features or products. For more on A/B testing, see this guide on A/B testing. With companies running up to 100 experiments weekly, designing and analyzing these tests can be repetitive and time-consuming. That’s why I automated this process using AI agents.
- Build SQL pipelines to extract A/B test data
- Perform exploratory data analysis (EDA) to select appropriate statistical tests
- Write Python code to run tests and visualize results
- Generate recommendations (e.g., rollout decisions)
- Present findings via Excel, documents, or slides to stakeholders Steps 2 and 3 are the most time-intensive because experiment results can be complex and sometimes contradictory. For instance, an image ad might boost short-term purchases, while a video ad could improve long-term retention and revenue. This requires deeper analysis, simulations, and multiple statistical techniques.
- I use Cursor, an AI-powered code editor that accesses codebases and writes or edits code automatically.
- Cursor, via the Model Context Protocol (MCP), accesses the data lake where raw experiment data flows.
- It builds pipelines to process experiment data and joins it with other relevant tables.
- Cursor performs EDA and selects the best statistical methods automatically.
- It runs the tests and generates a comprehensive, stakeholder-friendly HTML report. This end-to-end automation reduces manual intervention significantly. While I still review the AI’s work and results, the time saved is substantial. However, the process required extensive prompt engineering and example curation to reduce AI hallucinations and ensure accuracy. It took about a week of iteration before the workflow was reliable. Now, with this AI agent, I can analyze A/B test results much faster, freeing time for other tasks and enabling quicker product decisions.
- A Complete Guide to A/B Testing in Python
- The Future of AI in Data Science
- Understanding AI Agents: A Comprehensive Overview
- How to Master Prompt Engineering for AI Agents
What Are AI Agents?
AI agents are systems powered by large language models (LLMs) that can autonomously perform tasks by planning and reasoning through problems. They automate complex workflows without needing explicit step-by-step instructions from users. For example, you might run a single command, and the AI agent executes an entire workflow end-to-end, making decisions and adapting as it progresses. This frees you to focus on other tasks without constant supervision.How I Use AI Agents to Automate Experimentation in Data Science
Experimentation is central to data science. Companies like Spotify, Google, and Meta run experiments before product launches to evaluate:Traditional Experiment Analysis Workflow (3 days to 1 week):
Automating Experiment Analysis with AI Agents:
Why You Must Learn AI Agents for Data Science
AI adoption is accelerating across organizations to speed up business decisions and product launches. Data scientists must adapt to stay relevant. Building AI-assisted workflows requires new skills: MCP configuration, AI agent prompting (distinct from simple ChatGPT prompting), and workflow orchestration. Although the learning curve is steep, the payoff is hours saved daily. If you are a data scientist or aspiring to be one, start learning AI agent workflows early. This is becoming an industry standard, not just a bonus skill. To begin, watch this step-by-step free guide on agentic AI.About the Author Natassha Selvaraj is a self-taught data scientist passionate about writing on all things data science. Connect with her on LinkedIn or visit her YouTube channel.
Source: Originally published at KDnuggets on August 15, 2025.