Agentic AI can be used as an autonomous digital research assistant: a researcher gives it a high-level objective, and the agent breaks it down into subtasks and executes a multistep plan, such as gathering press releases, social media data, and news articles, performing sentiment and thematic analysis, and synthesizing findings into a structured report. This automates laborious parts of the workflow and lets the researcher focus on interpreting findings and crafting the final narrative. The chapter also highlights deep research agents, which synthesize hundreds of sources into a single cited report, useful for literature reviews.

Key points

Agentic AI systems are autonomous, unlike models that react to discrete prompts, and can comprehend a high-level objective, decompose it into subtasks, and execute a multistep plan.
In qualitative research, an agent can automate data collection and analysis; for example, a researcher may instruct it to produce a comprehensive report on stakeholder response to a corporate initiative.
The agent independently gathers press releases, social media data, and news articles, performs sentiment and thematic analysis, and synthesizes all findings into a structured report.
This frees researchers from preliminary analysis, allowing them to concentrate on interpreting results and writing the final narrative.
Deep research, a specialized application of agentic AI, synthesizes information from hundreds of sources into one comprehensive, cited report, which can accelerate literature reviews.

According to the chapter, how can agentic AI be used in qualitative research?

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