Field-Level Computation and Lazy Resolution

How properties become atomic computation units and why this enables partial resolution, modularity, and zero-waste execution.

In Grasp, computation lives at the property level. Instead of binding logic to entire objects or services, each field is its own resolvable promise. When you ask for a field, you awaken its resolver. When you don’t, nothing happens.

Why Fields Are Sovereign

Traditional systems often require you to load an entire object to compute a single value. That creates entanglement and waste. In Grasp, each field is independent:

`content` resolves when accessed.
`vector` resolves only when embeddings are requested.
`summary` triggers AI only when asked.

You don’t need to predict dependencies. You only need to ask for the property you care about.

The Benefits

No entanglement: You don’t accidentally trigger unrelated computations.
Partial resolution: Missing fields don’t break a node.
AI-friendly querying: Agents can probe selectively.
Modular processing: Resolvers are isolated and swappable.
Zero waste: Expensive work runs only when needed.

Resolvers as Latent Functions

Resolvers are dormant until invited by shape. They are not imperative tools you call; they are latent computations tied to fields. This transforms the graph into a set of askable truths, where each field is a contract: “If you ask me, I will try to produce this.”

Practical Example

You declare a `summary` field on `Transcript`, attach a resolver, and leave the graph minimal. When you query `summary`, the resolver runs. If it succeeds, you can decide whether to persist the result. If not, the rest of the node still stands.

Result: Computation as a Living Surface

Computation becomes a surface you touch rather than a pipeline you orchestrate. You ask for what you want, and the system resolves only that. This keeps complexity local and makes systems resilient and readable.