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AI agent for ux researchers

Research Insight Follow-Up Agent

Keep every research recommendation tracked until it is built, changed on purpose, or consciously dropped.

MonitoringProduct development
See how it worksGet access
Research Insight Follow-Up Agent: what goes in, what the agent does and what you get

What it does

A research readout ends and the recommendations quietly disappear. This agent logs each recommendation, links it to a product ticket, and checks the ticket status on a schedule. It notes when an item is closed, built differently from the recommendation, or dropped without a decision. It compares what was shipped with the original finding and flags when the fix may not solve the problem. It writes a status update for stakeholders and lists the items that need a decision. You approve the update before it is sent. Edge case: a ticket marked done shipped a different button label than the study recommended, and the agent shows the difference.

How it works

Follow the arrows from top to bottom. The orange dashed arrow is the loop: when a check fails, the agent goes back and tries again.

Start and resultWhat it doesA check on its own workWaits for your OKGoes back and retries
Yes, continueYes, continueApprovedNoNo 1 STARTS WHEN Research readout is finished 2 USES A TOOL Log each recommendation with its evidence 3 USES A TOOL Search the ticket system for matching tickets 4 CHECKS THE RESULT Does every recommendation have a ticket? If not: Mark items with no ticket and draft a ticket foreach. Back to step 3. 5 DOES Check ticket status and release notes on schedule 6 DOES Compare what shipped with the recommendation 7 CHECKS THE RESULT Does the shipped work match the finding? If not: Flag the differences and note the risk. Back tostep 4. 8 DOES Draft a status update with items needing a decision 9 YOU APPROVE Researcher approves the update 10 USES A TOOL Send the update to stakeholders 11 RESULT Recommendation tracker and update
Read the steps as a list
  1. Research readout is finished
  2. Log each recommendation with its evidence
  3. Search the ticket system for matching tickets
  4. Does every recommendation have a ticket?If not: Mark items with no ticket and draft a ticket for each. Back to step 3.
  5. Check ticket status and release notes on schedule
  6. Compare what shipped with the recommendation
  7. Does the shipped work match the finding?If not: Flag the differences and note the risk. Back to step 4.
  8. Draft a status update with items needing a decision
  9. Researcher approves the updateThe agent waits here for your OK.
  10. Send the update to stakeholders
  11. Recommendation tracker and update

How it decides

It matches each recommendation to a ticket by link or title, then reads status and release notes. Items with no ticket after two weeks are flagged.

  • No ticket after 14 days is flagged
  • A shipped item is checked against the original recommendation
  • Dropped items need a recorded reason
  • Update every two weeks until all items are closed

Make it yours

Every agent is a starting point. You choose these settings for your own situation.

  • Tracking period (default 3 months)
  • Update frequency (default every 2 weeks)
  • Ticket system and project
  • Stakeholder list
  • Days before an item with no ticket is flagged

What keeps you in control

It always asks you first

  • Status update
  • Any new ticket the agent drafts

Hard limits

  • Never closes or edits tickets
  • Never changes a recommendation
  • Shows the evidence behind each flag

It stops when

  • Done: all items closed, changed on purpose or dropped with a reason
  • Stop: 3 months passed; open items are handed over

Set it up

We guide you through the set-up, step by step

Members get the full set-up guide for this agent. No technical skills needed: you copy, paste and upload.

10 minto set it up in your AI
5 AIsChatGPT, Claude, Copilot, Gemini, Grok
  • One set of instructions to paste into your AI, with the clicks for ChatGPT, Claude, Microsoft 365 Copilot, Gemini and Grok
  • The agent then walks you through connecting your own data, one source at a time
  • A downloadable copy with the flow chart, the rules and the full guide
Get access to this agent

An example run

What happensAfter a checkout study, the agent logged 9 recommendations. Two had no ticket after two weeks, so the check failed and it drafted tickets. Later, a ticket for clearer shipping costs was closed, but the release notes showed the cost still appeared only at the last step. The agent flagged the mismatch in the update. The researcher approved it.

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