Agentic User Research
Agentic user research is the research half of a delivery loop that agents run. It turns real user behavior into evidence that a builder or coding agent can safely act on.
UserTold.ai is 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 entity model and lifecycle live in Core Concepts. 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 Findings in the backlog;
- reviewed Findings can be sent explicitly to Linear product intake or GitHub delivery.
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 a draft Finding is reviewed for project-aware review.