State of Agentic Data Science 2026 Survey

We are studying how AI agents are changing real data work: analysis, coding, modeling, reporting, verification, and decision-making. Target length: 5-10 minutes. The results will be shared publicly. If you leave your email, we'll send you the findings and occasional updates from Vanishing Gradients and PyMC Labs on Agentic Data Science.

1. What best describes your role?

Analytics engineer

Research scientist

Data/AI leader or manager

Software engineer working with data

2. How long have you worked in data, analytics, ML, or AI?

3. What kind of organization do you work in?

Consultancy or agency

Academic or research institution

Government or nonprofit

Independent consultant / freelancer

4. How often do you use AI tools or agents for data work?

A few times a month

5. Which AI tools do you use for data work?

Notebook-native AI assistant

Custom agent workflow

I do not use AI tools for data work

6. What do you use AI tools or agents for?

Writing or debugging code

Data cleaning and wrangling

Exploratory data analysis

Modeling or feature engineering

Code review or refactoring

Writing reports or summaries

Explaining concepts or learning

Planning analysis or problem framing

I do not use them for data work

Strategic brainstorming

Preparing slides or stakeholder communication

7. Where do AI agents help you most?

Moving faster on routine code

Learning unfamiliar tools or libraries

Improving reports or explanations

Refactoring or cleaning up messy work

Building pipelines or repeatable workflows

Thinking through analysis plans

Reviewing or critiquing work

They do not help much yet

8. Where do AI agents fail you most often?

They misunderstand the data

They produce plausible but wrong analysis

They write code that runs but answers the wrong question

They lose context in longer workflows

They make bad assumptions about the business problem

They struggle with statistics or uncertainty

They create brittle or hard-to-maintain code

They are hard to connect to my tools, files, or databases

I do not trust them enough to use them seriously

9. How do you usually verify AI-generated code or analysis?

I run it and inspect the output

I compare against known-good results

I write tests or checks

I ask another AI tool or agent to critique it

I ask a human colleague to review it

I only verify high-risk work

I do not formally verify it

10. Compared with a year ago, how much have AI agents changed your workflow?

11. Which statement feels closest to your current situation?

AI agents are mostly a faster autocomplete for me.

AI agents help with specific tasks, but I still drive the whole workflow.

AI agents now change how I plan and execute data work.

I am building repeatable agentic workflows around my data work.

I am not using AI agents seriously yet.

12. Walk us through the last time you used an AI agent for a real data task. What were you trying to do, and what happened?

13. What do you wish AI agents could do for your data work that they cannot reliably do yet?

14. How do you feel about the impact of AI agents on your data science work over the next 12 months?

Cautiously optimistic

Not expecting much change