Agentic User Research

Agentic user research is the research half of an agentic delivery loop. It turns real user behavior into evidence that a builder or coding agent can safely act on.

UserTold.ai is built for teams that already know how to ship. The missing input is usually not more velocity; it is a trustworthy issue that says what users tried, where they got stuck, and what they said in their own words.

What makes research agentic

Traditional research can end as notes, clips, or a report. Agentic research produces structured, source-linked evidence that a human or coding agent can inspect before deciding what should become delivery work.

The canonical entity model and lifecycle live in Core Concepts. The practical distinction is that automation does not replace product judgment:

• the interview preserves what the participant said and did;
• extraction creates evidence, not instructions;
• review confirms whether related evidence describes one current product problem;
• evidence grouping creates draft Work in the backlog;
• only Work reviewed with project context and marked ready enters Linear or GitHub.

Where UserTold is useful

Use agentic research where a delivery agent needs a trustworthy problem statement:

• onboarding or activation friction;
• pricing and packaging comprehension;
• complex setup and integration flows;
• feature validation against a real workflow;
• churn decisions and persistent workarounds.

It is less useful when the team only needs broad brand research or a one-off opinion survey.

Start

Choose how you want to meet users: observe ordinary use, test a specific feature or task, or invite an open conversation. Design the smallest study that supports that encounter, then run the participant flow yourself before inviting users. Use the Study Design Guide for the script and Interviews to Issues when draft Work is ready for project-aware review.