Modern data science workflows continue to evolve from conventional automation systems to systems that are able to plan activities, operate tools, perform actions, and assess outcomes with a little or no human involvement. AI agents can convert any business query into a series of logically sequenced steps leading to the output of a result and verify it.
This change is evident in various data teams. Based on the report by dbt Labs “2026 State of Analytics Engineering,” 72% of the respondents say that they value AI-aided coding in their development activities. The survey results indicate that such features are being used more and more in everyday data procedures.
AI agent workflows usually operate in a permanent cycle of performing the following activities like setting a goal, determining necessary actions, executing operations using tools like Python or SQL, assessing the results, and modifying actions.
AI-based solutions can assist in various phases of the data science pipeline and include tasks like data preparation, feature engineering, model building, assessment, and reporting. The key feature of AI programming solutions lies in the fact that they can be used by one agent or cooperative agents conducting specific tasks under the supervision of a data scientist.
This infographic explores how AI agents work across data science workflows and the skills professionals need to work effectively with them.
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