Skip to main content
This guide is the authored ontology path. Use it when task-facing structure should be explicit and reviewed before use. If the task boundary is clear but you want Adapt-1 to form the learner-facing structure from admitted evidence, start with Discovery instead. A Domain is still the stable public contract and scope for the task. Authored ontology is one way to populate that scope, not a requirement for using Domains. This choice does not determine the acquisition schedule: an authored Domain can learn from zero during a run, and a Discovery Domain can form state in a separate acquisition phase before frozen use. A Domain is not a prompt, a task solution, an environment, or a complete architecture description.
Start with the application contract you already have. Most Domains should use defaults. Add learner configuration only after the interaction loop is clear in ordinary language. A Domain is not required when the application only needs continuing evidence or constraints without a declared task grammar.

Define five things

These five answers are enough to begin. Entities, relations, policies, hypotheses, transition mappings, grouping, and support controls are ways to express the answers when the task needs them. They are not prerequisites for understanding a Domain.

Copy this worksheet

Example:
This is a design worksheet, not a universal API request body.

Follow the example through one complete loop

The worksheet above describes one work-routing Domain. The complete interaction keeps the Domain, the current observation, the Core result, and the external consequence separate. The selected route returned by Core, the route committed by the application, and the route executed externally are separate records. Preserve all three when validation or overrides can change the value.

Adaptation in three frames

When applicable evidence is admitted, support, learned relationships, policy or transition state, and a later result may change. The later result may also remain the same. Within the declared lifecycle, the Domain contract, public vocabulary, result type, and ownership boundary stay fixed. External state and external execution remain outside Core. A later result is not guaranteed to change or improve after one event. Verify event eligibility, confirm learner admission where exposed, inspect the applicable state change when documented, and preserve the later result separately.

What belongs in the Domain

Include public task semantics that remain stable across the intended scope: A Domain may declare that completed is a positive native outcome. It should not declare that a particular current action will cause completion unless that rule is intentionally public task information. A fresh Domain can begin with no accumulated task history. Supplying the vocabulary is not supplying the task-specific mapping. It gives Core the interface needed to participate.

Define one complete loop

Use the smallest loop that matches the application:
Record the returned value, the value the application actually selected, and the exact operation executed. They may differ because of validation, overrides, ties, or downstream constraints. The consequence must be attributable to the applicable event or decision. Do not attach a later outcome to an earlier choice merely because both occurred in the same session.

Choose learning only after the loop exists

Ordered data is not automatically sequential learning. Sequential credit fits only when an earlier action changes the state encountered later and a later outcome should revise that earlier action.
Transition prediction, bounded objective scoring, and action value are different:
A Domain can use more than one relationship, but each should have a separate evidence and result meaning.

One Domain or several

Reuse one Domain when all of these remain compatible:
  • Public task semantics.
  • Result contract and units.
  • Consequence or feedback meaning.
  • Event and episode boundary.
  • State retention and evaluation lifecycle.
Use separate Domains when any of those meanings must not mix. Inputs coming from the same dataset, application, device, or environment do not require one shared Domain. Different target geometries, feedback meanings, or evaluation boundaries may need isolated state. One Core instance can retain several Domains. Every operation addresses one domain_id, so their evidence and learned state remain separate.

State lifecycle

Name the lifecycle before evaluation: State carried from an earlier pass or episode inside the same zero-start run is within-run retention, not held-out transfer. Cold does not mean no Domain definition. Frozen does not mean no learned state. Transfer does not mean the task contract changed.

Minimal route map

The Domain returned by create or read is the resolved contract and may include defaults filled by Adapt-1. Send only the fields your integration needs. Returned defaults do not need to be copied into later create requests. A successful event write does not prove that every configured learner accepted the event. Advanced workflows should inspect learner admission and the applicable learner version before interpreting an unchanged result.

Domain smell test

Correct the design before implementation when any of these are true:

Choose how a Domain learns

Compare multistep action, hidden-process discovery, and structured prediction patterns.

Transition learning, advanced

Configure an observable before-state, action, target, support, grouping, and abstention contract.

Learn from outcomes

Bind decisions, commitments, execution, consequences, and scoped feedback.

Run an inspectable workflow

Preserve ordered provenance and verify the selected state lifecycle.