AI Builders: Stop Passing Around Text. Give AI Work a Data Model.

Language models are good at reconstructing meaning from prose, which makes it tempting to represent AI-generated work as text. But for systems that need to reason across many artifacts over time, repeatedly recovering structure from text is a weak foundation. If AI-generated work is going to persist, be reused, and influence downstream decisions, it needs a data model.

AI systems are already producing that kind of durable work: research findings, plans, recommendations, decisions, approvals, risk assessments, summaries, and other outputs that people and agents may rely on later. Yet most of it still gets passed around as text.

One model produces a paragraph, another reads it, and a third may summarize or reinterpret it. As that work moves through the system, the original structure can get lost, leaving later models to infer what kind of thing they received and how it relates to everything else.

1. An Artifact Should Have a Type

Consider the difference between a recommendation and a decision. They may both be represented as Markdown and may even contain similar language, but they are not the same thing.

A recommendation proposes an action, while a decision records that an action was chosen. An approval authorizes something, a risk identifies a potential problem, evidence supports a claim, and a plan defines intended work.

Those differences matter to the system even if a model can infer them from prose. Data systems learned a long time ago that important distinctions should be represented explicitly rather than repeatedly inferred from generic text.

AI work should follow the same principle. Instead of producing generic text, a model might produce a Recommendation, Decision, Risk, Claim, Evidence, or Plan, with each type carrying its own fields, validation rules, permissions, lifecycle, and expected relationships to other artifacts.

The text is still there, but it no longer has to carry the entire burden of meaning. The surrounding system now knows what kind of thing it is dealing with.

2. Typed Artifacts Make Relationships Explicit

Once artifacts have types, relationships between them can become part of the system rather than something buried inside prose. A claim can be supported by evidence, a recommendation can be based on several findings, a decision can accept or reject a recommendation, and a risk can affect a plan.

Those relationships are already present in real work, but today we often force the model to rediscover them every time it reads the surrounding text. That adds both cost and uncertainty to workflows that could preserve those relationships directly.

A system that already knows how one artifact relates to another does not need to infer the relationship from wording, dates, or nearby text each time. It can use those relationships as part of the application state.

This is the role of a semantic layer over AI-produced artifacts. The artifact type tells the system what something is, while the semantic layer captures how that artifact relates to the rest of the work.

3. Provenance Becomes Much More Useful

We already talk a lot about provenance in AI, but provenance attached to unstructured text only gets us so far. It is helpful to know that a paragraph came from a particular model run or source document, but structured artifacts let provenance become part of the work itself.

A recommendation might record which source material and intermediate findings shaped it, who reviewed it, which model produced it, and which later decisions referenced it. If a downstream result looks wrong, the system can trace through the artifacts that contributed to it rather than reconstructing the history from transcripts.

As agents produce inputs for other agents, preserving origin and status becomes increasingly important. Generated content can otherwise lose its identity as it moves through a system, especially after it has been summarized, rewritten, or incorporated into later work.

A model-generated hypothesis can start looking like a fact, a recommendation can start looking like an approved decision, and an old conclusion can remain in circulation after a newer one has replaced it. Typed artifacts give the system a way to preserve those distinctions.

4. Change Can Propagate Through the Work

A semantic layer over artifacts also gives AI systems a better way to handle change. Imagine that a source document is corrected and the system needs to determine which downstream work relied on the old version.

If all of that work is stored as documents, answering the question requires another round of search and model interpretation. If the artifacts and relationships are explicit, the system already knows which pieces of work are connected.

This makes invalidation, review, and selective recomputation much easier. A changed input does not necessarily require rerunning an entire workflow, and the system does not have to blindly trust every downstream output either.

It can identify the affected artifacts and determine what needs to be reviewed or regenerated. Data engineering has relied on explicit dependencies for exactly this reason, because connected work is much easier to manage when the system knows what depends on what.

5. This Is More Than Structured Output

Structured output is a useful first step because it gives a model response a predictable shape. Typed artifacts go further by assigning that output an identity, a lifecycle, and a defined role in the larger system.

A JSON object may exist only long enough for the next step to consume it. An artifact can persist across runs, accumulate reviews and annotations, be superseded by a newer version, and stay connected to the other artifacts around it. The schema describes its fields; the artifact model carries its history and relationships.

Once multiple artifact types share the same semantic model, they can function as durable application state rather than temporary payloads between model calls.

A New Kind of Data

AI is producing a category of data that traditional software created much less frequently: machine-generated work products that contain interpretation, judgment, synthesis, and recommendations. We should not treat those outputs as disposable text simply because models happen to communicate through language.

If the work is important, it deserves identity, and if different kinds of work behave differently, they deserve types. We have spent the first phase of generative AI getting models to produce better text, and the next phase will require better systems for representing what that text actually is.