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 often ends as notes, clips, or a slide deck. Agentic research needs a tighter output:
- a study that captures the right behavior
- a transcript and timeline that preserve what happened
- extracted evidence with quotes and context
- evidence review packets that preserve source context
- project-aware promotion into work items connected to delivery tools
- Linear completion sync that resolves current evidence after delivery
- future recurrence review when similar evidence appears again
The output is not "users want better onboarding." It is a verified evidence review packet that can become an evidence-backed work item such as "clarify API key setup before the widget embed step," linked to the interviews, quotes, and observed actions that justify it.
The UserTold loop
- Define a study with goals, intake rules, and a script.
- Run in-product interviews with
talk,speak, andobservesegments. - Extract evidence such as struggling moments, desired outcomes, and workarounds.
- Cluster evidence into review packets.
- Verify packets against source context and project knowledge before promoting them to delivery work.
- Sync Linear completion, resolve the current evidence, and watch future interviews for possible recurrence.
This keeps the delivery loop grounded in user reality instead of internal guesses.
Example: turning onboarding confusion into work
A builder embeds the UserTold widget on the onboarding flow and runs five interviews with new users.
The study starts with a short talk segment to understand the user's goal, moves into an observe segment while the user connects a provider account, then debriefs the exact moments where the user paused or backtracked.
Evidence might include:
| Evidence | Type | Review packet candidate |
|---|---|---|
| "I do not know which key this wants." | struggling_moment | Users cannot identify which provider key setup requires |
| User opens docs twice during the same step | workaround | Users leave setup to infer key requirements from docs |
| "I expected this to test the connection." | desired_outcome | Users need confidence that saved credentials actually connect |
The packet can then be verified against the source moments and current setup roadmap. If the project-aware reviewer decides it is action-ready, the promoted work item can move into the team's normal delivery system with the source quotes and interview links attached.
Where UserTold is different
UserTold does not treat the AI interviewer as a magic product manager. The system preserves behavior first:
observemode stays silent so it does not bias the participanttalkmode asks planned follow-up questions- evidence retains quotes, timestamps, URLs, and confidence
- review packets remain inspectable before they are promoted or pushed to a tracker
- Linear completion resolves current evidence while preserving the original links
- future interviews can surface possible recurrence without claiming attribution
That makes the research useful to humans and agents. A coding agent can read a verified work item, inspect the linked evidence, and ship against a concrete user problem.
When to use it
Agentic user research is strongest when you need to decide what to fix next:
- onboarding and activation flows
- pricing or packaging comprehension
- feature validation before building too much
- churn-risk workflows
- complex setup, integration, or admin flows
It is less useful when the team only needs broad brand research or a one-off opinion survey.
Qualified next step
If you have a product flow where users already get stuck, start with the study design guide and build one study around that flow. If you are evaluating the full loop, use the how it works overview to map interview, evidence, work item handoff, Linear completion, and recurrence review before inviting participants.