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AI agent for ux researchers

Qualitative Coding Consistency Agent

Reach a stable codebook with agreement high enough to trust the themes.

Quality controlProduct developmentScience and research
See how it worksGet access
Qualitative Coding Consistency Agent: what goes in, what the agent does and what you get

What it does

When two researchers code the same quote differently, themes become unreliable. This agent applies your codebook to the interview transcripts, then compares its codes with a sample that a person has already coded. It measures agreement and lists each quote where they disagree. For each disagreement, it checks whether the code definition is unclear and proposes a sharper wording or an example. It then recodes and measures agreement again until it meets your target. You approve the final codebook. Edge case: the codes 'confusion' and 'hesitation' keep overlapping, and the agent proposes a clear boundary with two examples.

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, continueApprovedYes, continueNoNo 1 STARTS WHEN First transcripts are coded by a human 2 USES A TOOL Load the codebook and the transcripts 3 USES A TOOL Apply the codebook to the sample transcripts 4 DOES Compare the codes with the human-coded sample 5 CHECKS THE RESULT Is agreement at or above the target? If not: List the codes and quotes with the lowestagreement. Back to step 4. 6 DOES Propose sharper definitions with examples 7 YOU APPROVE Researcher approves the changes to the codebook 8 USES A TOOL Recode the sample with the revised codebook 9 CHECKS THE RESULT Has agreement improved to the target? If not: Repeat for the codes still below target, up tothe round limit. Back to step 6. 10 USES A TOOL Apply the final codebook to the remainingtranscripts 11 RESULT Final codebook and agreement report
Read the steps as a list
  1. First transcripts are coded by a human
  2. Load the codebook and the transcripts
  3. Apply the codebook to the sample transcripts
  4. Compare the codes with the human-coded sample
  5. Is agreement at or above the target?If not: List the codes and quotes with the lowest agreement. Back to step 4.
  6. Propose sharper definitions with examples
  7. Researcher approves the changes to the codebookThe agent waits here for your OK.
  8. Recode the sample with the revised codebook
  9. Has agreement improved to the target?If not: Repeat for the codes still below target, up to the round limit. Back to step 6.
  10. Apply the final codebook to the remaining transcripts
  11. Final codebook and agreement report

How it decides

It computes agreement per code and works on the codes with the lowest scores first, using the actual disagreeing quotes as evidence.

  • Target agreement of 80% per code (default)
  • Work on the lowest-agreement codes first
  • Merge codes that cannot be separated
  • Stop after 4 revision rounds (default)

Make it yours

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

  • Target agreement (default 80%)
  • Sample size coded by hand
  • Maximum rounds (default 4)
  • Agreement method
  • Which codes may be merged

What keeps you in control

It always asks you first

  • Codebook changes
  • Final themes

Hard limits

  • Never changes a human's codes
  • Keeps every codebook version
  • Flags small samples

It stops when

  • Done: agreement reaches target
  • Stop: round limit reached; researcher decides on merging codes

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 happensOn 5 coded interviews, agreement was 68% overall. 'Confusion' and 'hesitation' scored 41% and 47%. The agent proposed a boundary based on whether the user paused or asked a question. The researcher approved it. The recode gave 74%, so the check failed. A second revision added examples and reached 83%.

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