AI documentation is the written record that explains what a model or agent does, what data shaped it, how it was tested, and where it fails. The four artifact types you'll run into most are model cards, eval reports, agent cards, and system/data cards. The problem is that most teams write them once, badly, under deadline pressure — then discover six months later that nobody can answer a regulator's question, a security reviewer's question, or their own engineer's question about why a model behaves the way it does.
This guide walks through what each artifact actually needs to contain, why the usual approach fails, and how to build documentation that holds up when someone outside your team reads it cold. It's written for the person who just got handed "you own model documentation now" with no playbook.
What are the four core AI documentation artifacts?
They're not interchangeable, and the most common mistake is treating them as one document with different headers. Each answers a different question for a different reader.
- Model card — What is this model, what was it trained on, what's it for, and what are its known limitations? Primary reader: anyone consuming the model, including downstream engineers and procurement.
- Eval report — How does it actually perform, on what tests, with what results? Primary reader: whoever has to decide whether it's good enough to ship.
- Agent card — What can this agent do, what tools can it call, what are its boundaries and escalation paths? Primary reader: the people integrating it into a workflow with real consequences.
- System or data card — What's the broader pipeline, what data flows through it, what are the dependencies? Primary reader: auditors, security teams, and whoever inherits the system.
The reason the distinction matters: a model card that includes eval results becomes stale the moment you retrain. An eval report that restates the model card wastes the reader's time. Keep them separate, keep them linked, and version each one independently.
Why do most model cards fail the first time someone reads them?
Because they're written as a compliance checkbox rather than a decision aid. The tell is vagueness in exactly the places that matter. "Trained on a diverse dataset" tells a reader nothing. "Known limitations: may produce inaccurate outputs" is a sentence that could describe every model ever built.
A model card that works answers three questions concretely:
- What inputs were used? Name the sources, the time window, and the known gaps. If you excluded a category of data, say so and say why.
- What's the intended use, and what's explicitly out of scope? The out-of-scope section is the one people skip and the one that saves you later.
- What does failure look like? Not "the model may err" — describe the actual failure mode. Does it hallucinate citations? Does it degrade on long inputs? Does it perform worse for a specific subset of users?
That third point is where the real value sits. A model card that honestly documents a failure mode lets a downstream team design around it. One that doesn't means they discover it in production.
If your limitations section could be copy-pasted onto a different model without anyone noticing, it isn't a limitations section. It's filler.
How do you write an eval report that someone can actually act on?
An eval report is a measurement document, and measurement documents live or die on reproducibility. The reader needs to be able to see how you got the number, not just the number.
Structure it around five fields per evaluation:
- Task definition — what exactly was being measured, in plain language
- Dataset — what inputs, how many, how selected, and any known skew
- Metric — what you're scoring on and why that metric over alternatives
- Result — the outcome, with the baseline you're comparing against
- Interpretation — what the result means for the intended use, and what it doesn't tell you
That last field is the one teams omit, and it's the one that makes the report useful. A number without interpretation is a fact; a number with interpretation is a decision.
One practical note: keep a consistent eval suite across model versions. If you change the test set every time you retrain, you can't compare results, and the report becomes a series of unrelated snapshots. Version the eval suite itself and record which version each report used.
A worked example: documenting a support-triage agent
Say you're shipping an agent that reads incoming support tickets and routes them to one of four queues. Here's what the three artifacts look like in practice.
Agent card. Purpose: classify and route inbound tickets. Tools it can call: a ticket-lookup API and a queue-assignment API. Boundaries: it cannot close tickets, cannot send customer-facing replies, and escalates anything tagged "billing dispute" to a human regardless of confidence. Escalation path: confidence below threshold goes to a human queue. Known failure mode: short tickets under roughly ten words are frequently misrouted because there isn't enough context.
Eval report. Task: routing accuracy against a held-out set of tickets with known correct queues. Dataset: a labeled sample drawn from the last quarter, with a note that it skews toward English-language tickets. Metric: exact-match routing accuracy, compared against the human baseline. Result: reported as a range, not a single figure, because accuracy varies by queue. Interpretation: strong on three queues, weaker on the fourth, which is why the escalation rule exists.
Model card. The underlying classifier, its training inputs, intended use (internal routing only), out-of-scope use (any customer-facing decision), and the documented failure mode on short inputs.
Notice that no single document repeats the others. The agent card points to the eval report; the eval report points to the model card. That linking is what keeps the set maintainable.
Where does this approach break down?
Honestly, in three places.
Maintenance cost. Documentation that isn't updated is worse than none, because it creates false confidence. If you retrain monthly, you need a process that regenerates the eval report and flags the model card for review. Teams that skip this end up with docs describing a model that no longer exists.
Small teams. A five-person startup doesn't need four artifact types on day one. Write the model card and a lightweight eval report, and add agent cards when you actually ship an agent with tool access. Over-documenting early burns time you don't have.
Third-party models. When you're building on someone else's model, you can't document its training data because you don't have it. What you can document is your own layer: your prompts, your eval results, your known failure modes on your workload. Be explicit that upstream details are unavailable rather than guessing at them.
There's also a tooling question. If you're tracking many models and their documentation side by side, a structured registry beats a folder of scattered documents. This site maintains an internal database of 360 AI tools, each with a pricing and capability snapshot recorded at verification time, most recently verified on 2026-09-24 — the point being that even a snapshot goes stale, which is exactly why the verification date belongs in the record.
What should go in a documentation registry?
If you're managing more than a handful of models or agents, a registry keeps the artifacts findable. Each entry needs, at minimum:
- The artifact type and version
- The owner — a named person, not a team alias
- The date last reviewed
- The date the underlying model or data was last verified
- Links to related artifacts
That verification date field matters more than it looks. A capability claim recorded eighteen months ago may no longer be true, and without the date there's no way to know whether to trust it. Treat every snapshot as having a shelf life.
One more thing worth saying: the writing itself is often the bottleneck, not the analysis. If your team is spending more time drafting the prose of these documents than doing the evals behind them, that's a signal to templatize. Tools like AI-Mind take a described requirement and a chosen content type and handle the prompt engineering, which is occasionally useful for boilerplate-heavy sections — but it won't write your eval methodology for you, and it shouldn't.
Key Takeaways
- Model cards, eval reports, and agent cards answer different questions for different readers — don't merge them.
- A limitations section that could apply to any model is filler; name the actual failure mode.
- Eval reports need interpretation, not just numbers, or they don't support a decision.
- Version your eval suite so results stay comparable across model retrains.
- Every documentation record needs a verification date, because snapshots go stale.
The single highest-leverage habit here is writing the out-of-scope and failure-mode sections first. They're the hardest to write and the most valuable to read, and starting with them forces you to be specific before the comfortable prose takes over. If you only fix one thing this quarter, fix that.
Sources
- AI Tool Database, Internally verified snapshot of 360 AI tools with pricing and capability records, 2026. Most recent verification date 2026-09-24; used here to illustrate how capability snapshots age.
Frequently Asked Questions
What's the difference between a model card and an agent card?
A model card documents the model itself: training inputs, intended use, out-of-scope uses, and known failure modes. An agent card documents a system built on top of one or more models: what tools the agent can call, what actions it's forbidden from taking, its escalation paths, and how it behaves when uncertain. If your system only classifies or generates text, you likely need a model card. If it takes actions, you need both.
How often should AI documentation be updated?
Update the eval report every time you retrain or change the eval suite, and review the model card on the same cadence. Add a verification date to every record so readers know how current it is. Documentation that describes a model you no longer run is worse than no documentation, because it creates false confidence in stale claims.
Can you write a model card for a third-party model you didn't train?
Not fully — you don't have the training data or the original evaluation methodology. What you can document is your own layer: the prompts you use, your eval results on your workload, and the failure modes you've observed. State plainly that upstream details are unavailable rather than guessing at them. That honesty is more useful to a reviewer than a fabricated dataset description.