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AI agent for data scientists

Model Documentation Card Agent

A model card in which every figure traces to a record and matches the shipped version

Quality controlCreatingIT and development
See how it worksGet access
Model Documentation Card Agent: what goes in, what the agent does and what you get

What it does

Teams ship a model and discover the documentation describes an earlier version. This agent collects metrics, data sources, training dates, known limits and fairness results straight from the experiment records. It drafts a model card from them in the team's template. Then it checks every number in the card against its source, such as accuracy against the evaluation run and sample counts against the dataset. Contradictions are listed and the passage is rewritten from the record. It also checks that the card names the exact model version that is being released. The scientist and a reviewer approve before the card is published. Edge case: a fairness metric missing for one group is called out as a gap, not left out silently.

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 Model marked ready for release 2 USES A TOOL Read experiment records, evaluation results anddataset descriptions 3 DOES Draft the model card in the template 4 USES A TOOL Check each number and claim against its source 5 CHECKS THE RESULT Does every figure match its source? If not: rewrite the passage from the record and note thechange. Back to step 2. 6 CHECKS THE RESULT Does the card name the version being shipped? If not: find the release version and update allreferences. Back to step 3. 7 DOES List gaps such as missing fairness results or limits 8 USES A TOOL Ask the scientist to fill the gaps 9 YOU APPROVE Scientist and reviewer approve the card 10 USES A TOOL Publish the card with the release 11 RESULT Published card and change log
Read the steps as a list
  1. Model marked ready for release
  2. Read experiment records, evaluation results and dataset descriptions
  3. Draft the model card in the template
  4. Check each number and claim against its source
  5. Does every figure match its source?If not: rewrite the passage from the record and note the change. Back to step 2.
  6. Does the card name the version being shipped?If not: find the release version and update all references. Back to step 3.
  7. List gaps such as missing fairness results or limits
  8. Ask the scientist to fill the gaps
  9. Scientist and reviewer approve the cardThe agent waits here for your OK.
  10. Publish the card with the release
  11. Published card and change log

How it decides

A statement stays only if it is backed by a record. A mismatch is resolved in favor of the source record and logged.

  • Every figure must link to a source record
  • Use the evaluation on the held-out set, not training scores
  • Call out missing subgroup results as gaps
  • Flag any card older than the latest model version

Make it yours

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

  • Card template and sections
  • Source systems for metrics
  • Fairness groups to require
  • Who reviews before publishing

What keeps you in control

It always asks you first

  • Scientist and reviewer approval before publishing

Hard limits

  • Never invents metrics or limits
  • Never publishes without both approvals

It stops when

  • Done: card published with all figures verified
  • Stop: no evaluation record exists for the release version

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 happensFor version 2.3 of a churn model, the draft said accuracy was 0.91, but the evaluation run showed 0.88 on held-out data. The check failed, so the agent corrected the passage and logged it. It also found the card named version 2.2, fixed it, and listed that results for the under-25 group were missing. The scientist added them and the reviewer approved on 19 June.

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